Research Question
Goal of this analysis is to answer one of my four main research questions:
-Is variation in salinity, air temperature, or pH associated with changes in abundance or reproductive effort of Fucus distichus populations in SFE?
Note: I still don’t have the air temperature data completed so I’ll be focusing on salinity and pH. I do have water temeprature so I may look at that instead of air temp for now.

Data
Field data is the mean of these values per survey. Environmental data is the median of hourly median data inbetween field survey dates.

I will be using the combined environmental and field data at all sites. Right now I have the following match-ups for field site and water data source: China Camp and Paradise Cay, EOS and Point Chauncy, Richardson Bay and Brickyard Park, and Fort Point and Horseshoe Bay. I could match Paradise Cay with EOS instead of China Camp but that’s something I need to look into more.

Data for abundace: no.fuc.q (density: total number of fucus per quadrat), cover (percent conver fucus per quadrat), no.large.fuc.q (number of large fucus per quadrat), and no.small.fuc.q (number of small fucus per quadrat)

Data for reproductive effort: covcl.repro (cover class of reproductive tissue), dw.repro (dry weight of reproductive tissue), apices.repro (number of reproductive apices), perc.ra (percent of apices that are reproductive), avg.oog (average number of oogonia) and perc.rdw (percent of dry weight that is reproductive tissue)

Data for salinity: salinity (median salinity), daily.min.sal (daily minimun salinity), daily.max.sal (daily maximum salinity), daily.sal.range (daily salinity range), daily.med.sal (daily median salinity), min.daily.sal.lt5/10/15 (number of days with a minimun daily dalinity less than 5, 10, and 15), max.daily.sal.lt5/10/15 (number of days with a maximum daily salinity less than 5, 10, and 15) and daily.sal.range.gt5/10 (number of days with a daily salinity range greater than 5 and 10)

Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Breaking up main research question
Given these variables, I’m going to break up my research question into discrete subquestions that I can run individual tests for:
Q1. Effects of salinity and pH on abundance

Q2. Effects of salinity and pH on reproductive effort

Question for Karina:
-I’m not sure how to do every combination of all these variables? Should I see which ones have any significance and then try combos of those? Should I look at terms individually and then look at interactions with significate terms? –>because I’m not sure I didn’t look at a lot of interaction terms until I clarify this. For now the only interaction term I have is salinity:ph to keep it simple -Not sure if I should be looking at the lm or anova results for significance

Notes
Linear model google doc from Karina:

Plot interpretation

Reporting

Initial results summary:
Significate = P<0.05
Weak/slight = P<0.1
If no effect, not listed below. All variables tested are listed under “Data” section above
Not sure if I should be looking at the lm or anova results for these values. The difference between them is usually small but does make some values significant vs weakly significant

Q1. Effects of salinity and pH on abundance

Q2. Effects of salinity and pH on reproductive effort

Initial interpretation of results
(these are just initial impressions and not well articulated/thought out yet) -Salintiy has more of an impact on Fucus abundance while pH has more of an impact on fucus reproduction –>does this align with the results I see in my experiment? -Salinity impacts density but not cover, suggesting that cover is maintained even as density composition (size of thalli) changes. The composition changes but the cover remains. This is seen in my field work as well, density declined but cover remained pretty constant. -Density of small thalli are effected by changes to salinity but large thalli are not suggesting small thalli are driving the driving factor to why density is effected but not cover since larger thalli contribute more to cover than small thalli. Again,this pattern is also seen in my field work; total density pattern follows the small thalli density pattern more than large thalli. -Since small thalli are affected by salinity and large are not: This suggests that there’s some critical size that once fucus reaches it’s more tolerant to salinity changes –> suggestion for future studies -Cover class of reproductive tissue (percent cover that is reproductive tissue) is affected by salinity and I wonder if this trend is also mainly driven by the amount of small thalli? Since small thalli tend to have less reproductive tissue than large thalli -Need to look at the direction of the relationship of ph and repro (linear equation)

Set up

rm(list=ls())

library(tidyverse)
library(ggpubr)
library(scales)
library(chron)
library(plotly)
library(taRifx)
library(aweek)
library(easypackages)
library(renv)
library(here)
library(ggthemes)
library(gridExtra)
library(patchwork)
library(tidyquant)
library(recipes) 
library(cranlogs)
library(knitr)
library(openair)

Read in data

#read in data
all<-read.csv("C:/Users/chels/Box Sync/Thesis/Data/Working data/Bouy data/envi.field.all.csv", header = TRUE, sep=",", fileEncoding="UTF-8-BOM", stringsAsFactors = FALSE)

####Q1. Effects of salinity and pH on abundance####
Redundant from above, just placing it here for reference

Data for abundace: no.fuc.q (density: total number of fucus per quadrat), cover (percent conver fucus per quadrat), no.large.fuc.q (number of large fucus per quadrat), and no.small.fuc.q (number of small fucus per quadrat)

Data for salinity: salinity (median salinity), daily.min.sal (daily minimun salinity), daily.max.sal (daily maximum salinity), daily.sal.range (daily salinity range), daily.med.sal (daily median salinity), min.daily.sal.lt5/10/15 (number of days with a minimun daily dalinity less than 5, 10, and 15), max.daily.sal.lt5/10/15 (number of days with a maximum daily salinity less than 5, 10, and 15) and daily.sal.range.gt5/10 (number of days with a daily salinity range greater than 5 and 10)

Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

####Q1.1 Effect of salinity and pH on total density#### Different salinity terms first

Effect of pH and salinity on density

lm1 <- lm(no.fuc.q ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##     168.283      -12.429      -22.897        1.877
summary (lm1)
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -38.63 -25.34 -10.50  10.54 114.39 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  168.283   1445.158   0.116    0.908
## salinity     -12.429     64.140  -0.194    0.847
## ph           -22.897    182.826  -0.125    0.901
## salinity:ph    1.877      8.119   0.231    0.818
## 
## Residual standard error: 34.95 on 41 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2075, Adjusted R-squared:  0.1495 
## F-statistic: 3.578 on 3 and 41 DF,  p-value: 0.02182
anova (lm1)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity     1  12797 12797.3 10.4788 0.002393 **
## ph           1    246   246.0  0.2015 0.655910   
## salinity:ph  1     65    65.3  0.0535 0.818312   
## Residuals   41  50072  1221.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on density, interaction term removed

lm2 <- lm(no.fuc.q ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    -157.345        2.399       18.307
summary (lm2)
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.608 -25.180  -9.907  13.696 114.562 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -157.3453   319.9981  -0.492  0.62549   
## salinity       2.3992     0.7271   3.299  0.00198 **
## ph            18.3068    40.3247   0.454  0.65218   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.55 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2064, Adjusted R-squared:  0.1687 
## F-statistic: 5.463 on 2 and 42 DF,  p-value: 0.007782
anova (lm2)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity   1  12797 12797.3 10.7204 0.002126 **
## ph         1    246   246.0  0.2061 0.652177   
## Residuals 42  50137  1193.7                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on density: daily minimum salinity

lm3 <- lm(no.fuc.q ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = no.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##       -100.25           2.24          12.18
summary (lm3)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.950 -23.138  -7.576  11.559 112.190 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -100.25     315.20  -0.318   0.7520   
## daily.min.sal     2.24       0.65   3.446   0.0013 **
## ph               12.18      39.85   0.306   0.7614   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.23 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.221,  Adjusted R-squared:  0.1839 
## F-statistic: 5.959 on 2 and 42 DF,  p-value: 0.00527
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.min.sal  1  13856 13855.8 11.8245 0.001332 **
## ph             1    109   109.5  0.0934 0.761368   
## Residuals     42  49215  1171.8                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on density: daily maximum salinity

lm4 <- lm(no.fuc.q ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = no.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      -193.164          2.629         21.211
summary (lm4)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37.28 -24.82 -10.18  11.83 115.60 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -193.1639   331.5842  -0.583  0.56331   
## daily.max.sal    2.6294     0.9233   2.848  0.00678 **
## ph              21.2111    41.5612   0.510  0.61247   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35.49 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1625, Adjusted R-squared:  0.1226 
## F-statistic: 4.074 on 2 and 42 DF,  p-value: 0.02414
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##               Df Sum Sq Mean Sq F value  Pr(>F)   
## daily.max.sal  1   9938  9937.8  7.8880 0.00752 **
## ph             1    328   328.2  0.2605 0.61247   
## Residuals     42  52914  1259.9                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on density: daily salinity range

lm5 <- lm(no.fuc.q ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##          79.581           -2.551           -2.934
summary (lm5)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.438 -22.839 -10.380   9.927 115.644 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       79.581    343.176   0.232   0.8177  
## daily.sal.range   -2.551      1.255  -2.032   0.0485 *
## ph                -2.934     43.385  -0.068   0.9464  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.99 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.09022,    Adjusted R-squared:  0.0469 
## F-statistic: 2.083 on 2 and 42 DF,  p-value: 0.1373
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                 Df Sum Sq Mean Sq F value Pr(>F)  
## daily.sal.range  1   5694  5694.1  4.1606 0.0477 *
## ph               1      6     6.3  0.0046 0.9464  
## Residuals       42  57480  1368.6                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on density: daily median salinity

lm6 <- lm(no.fuc.q ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = no.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      -156.853          2.388         18.322
summary (lm6)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.05 -24.11 -10.00  13.09 114.66 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -156.8530   320.3040  -0.490  0.62689   
## daily.med.sal    2.3878     0.7271   3.284  0.00207 **
## ph              18.3219    40.3644   0.454  0.65223   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.58 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2049, Adjusted R-squared:  0.1671 
## F-statistic: 5.413 on 2 and 42 DF,  p-value: 0.0081
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.sal  1  12701 12701.2  10.620 0.002221 **
## ph             1    246   246.4   0.206 0.652229   
## Residuals     42  50233  1196.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on density: number of days with a daily minimun salinity less than 5

lm7 <- lm(no.fuc.q ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          -22.7630            -0.0223             8.4803
summary (lm7)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.413 -24.922 -14.090   5.789 119.291 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -22.7630   373.1041  -0.061    0.952
## min.daily.sal.lt5  -0.0223     0.7565  -0.029    0.977
## ph                  8.4803    47.5925   0.178    0.859
## 
## Residual standard error: 38.77 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0007751,  Adjusted R-squared:  -0.04681 
## F-statistic: 0.01629 on 2 and 42 DF,  p-value: 0.9838
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1      1    1.24  0.0008 0.9772
## ph                 1     48   47.72  0.0317 0.8594
## Residuals         42  63131 1503.13
plot (lm7)

Effect of salinity and pH on density: number of days with a daily minimun salinity less than 10

lm8 <- lm(no.fuc.q ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##          -168.5631             -0.8751             28.3393
summary (lm8)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.98 -23.97 -13.37  10.63 109.06 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -168.5631   369.8671  -0.456    0.651
## min.daily.sal.lt10   -0.8751     0.7072  -1.237    0.223
## ph                   28.3393    47.2553   0.600    0.552
## 
## Residual standard error: 38.08 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0359, Adjusted R-squared:  -0.01001 
## F-statistic: 0.782 on 2 and 42 DF,  p-value: 0.464
anova (lm8)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1   1747 1746.66  1.2044 0.2787
## ph                  1    522  521.59  0.3596 0.5519
## Residuals          42  60912 1450.29
plot (lm8)

Effect of salinity and pH on density: number of days with a daily minimun salinity less than 15

lm9 <- lm(no.fuc.q ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##           -118.735              -1.195              22.767
summary (lm9)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -44.91 -23.68 -12.25  12.08 103.57 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        -118.7350   345.9250  -0.343   0.7331  
## min.daily.sal.lt15   -1.1946     0.6347  -1.882   0.0667 .
## ph                   22.7675    44.0262   0.517   0.6078  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37.23 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.07849,    Adjusted R-squared:  0.0346 
## F-statistic: 1.789 on 2 and 42 DF,  p-value: 0.1797
anova (lm9)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)  
## min.daily.sal.lt15  1   4588  4588.0  3.3097 0.0760 .
## ph                  1    371   370.7  0.2674 0.6078  
## Residuals          42  58222  1386.2                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on density: number of days with a daily maximum salinity less than 5

lm10 <- lm(no.fuc.q ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          -29.7192            -0.0563             9.4085
summary (lm10)
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.570 -25.013 -14.270   6.033 118.948 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -29.7192   380.1293  -0.078    0.938
## max.daily.sal.lt5  -0.0563     0.7325  -0.077    0.939
## ph                  9.4085    48.5293   0.194    0.847
## 
## Residual standard error: 38.77 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0008949,  Adjusted R-squared:  -0.04668 
## F-statistic: 0.01881 on 2 and 42 DF,  p-value: 0.9814
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1      0    0.05  0.0000 0.9954
## ph                 1     56   56.49  0.0376 0.8472
## Residuals         42  63124 1502.95
plot (lm10)

Effect of salinity and pH on density: number of days with a daily maximum salinity less than 10

lm11 <- lm(no.fuc.q ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           -58.2233             -0.2112             13.2417
summary (lm11)
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37.43 -25.32 -15.13   6.47 117.33 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -58.2233   380.9538  -0.153    0.879
## max.daily.sal.lt10  -0.2112     0.7437  -0.284    0.778
## ph                  13.2417    48.6612   0.272    0.787
## 
## Residual standard error: 38.73 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.002669,   Adjusted R-squared:  -0.04482 
## F-statistic: 0.05619 on 2 and 42 DF,  p-value: 0.9454
anova (lm11)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1     58   57.52  0.0383 0.8457
## ph                  1    111  111.09  0.0740 0.7869
## Residuals          42  63012 1500.28
plot (lm11)

Effect of salinity and pH on density: number of days with a daily maximum salinity less than 15

lm12 <- lm(no.fuc.q ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##          -164.7073             -0.7008             27.5323
summary (lm12)
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.490 -26.062 -14.279   9.566 111.403 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -164.7073   381.7517  -0.431    0.668
## max.daily.sal.lt15   -0.7008     0.7131  -0.983    0.331
## ph                   27.5323    48.8189   0.564    0.576
## 
## Residual standard error: 38.33 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02322,    Adjusted R-squared:  -0.0233 
## F-statistic: 0.4991 on 2 and 42 DF,  p-value: 0.6106
anova (lm12)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1    999  999.39  0.6801 0.4142
## ph                  1    467  467.35  0.3181 0.5758
## Residuals          42  61714 1469.37
plot (lm12)

Effect of salinity and pH on density: number of days with a daily salinity range greater than 10

lm13 <- lm(no.fuc.q ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             -36.3536               -0.1021               10.3131
summary (lm13)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.549 -25.108 -14.147   6.209 118.482 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)          -36.3536   376.1979  -0.097    0.923
## daily.sal.range.gt10  -0.1021     0.7365  -0.139    0.890
## ph                    10.3131    48.0107   0.215    0.831
## 
## Residual standard error: 38.76 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.001211,   Adjusted R-squared:  -0.04635 
## F-statistic: 0.02546 on 2 and 42 DF,  p-value: 0.9749
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1      7    7.19  0.0048 0.9452
## ph                    1     69   69.33  0.0461 0.8310
## Residuals            42  63104 1502.47
plot (lm13)

Effect of salinity and pH on density: number of days with a daily salinity range greater than 5

lm14 <- lm(no.fuc.q ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##              -4.998               -1.291                8.654
summary (lm14)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.23 -25.70 -13.00  13.28 101.42 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          -4.9982   340.2736  -0.015   0.9884  
## daily.sal.range.gt5  -1.2914     0.6461  -1.999   0.0521 .
## ph                    8.6543    43.1123   0.201   0.8419  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37.05 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.08755,    Adjusted R-squared:  0.0441 
## F-statistic: 2.015 on 2 and 42 DF,  p-value: 0.146
anova (lm14)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                     Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt5  1   5476  5475.9  3.9894 0.05229 .
## ph                   1     55    55.3  0.0403 0.84187  
## Residuals           42  57649  1372.6                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on density: daily minimum ph

lm3 <- lm(no.fuc.q ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = no.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##       30.926        -5.500         2.361
summary (lm3)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.685 -24.501  -9.043   9.661 114.516 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   30.9262   432.5131   0.072  0.94334   
## daily.min.ph  -5.5004    54.9902  -0.100  0.92080   
## salinity       2.3613     0.7372   3.203  0.00259 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.63 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2027, Adjusted R-squared:  0.1648 
## F-statistic:  5.34 on 2 and 42 DF,  p-value: 0.008582
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.min.ph  1    504   504.2  0.4204 0.520282   
## salinity      1  12305 12305.2 10.2602 0.002594 **
## Residuals    42  50371  1199.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on density: daily maximum ph

lm4 <- lm(no.fuc.q ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = no.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     -222.049        26.099         2.435
summary (lm4)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.398 -25.512  -9.885  15.010 107.665 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -222.049    233.031  -0.953  0.34611   
## daily.max.ph   26.099     28.911   0.903  0.37182   
## salinity        2.435      0.723   3.368  0.00163 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.3 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2177, Adjusted R-squared:  0.1805 
## F-statistic: 5.845 on 2 and 42 DF,  p-value: 0.005761
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.ph  1    411   410.7   0.349 0.557832   
## salinity      1  13346 13345.6  11.341 0.001632 **
## Residuals    42  49424  1176.8                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on density: daily ph range

lm5 <- lm(no.fuc.q ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = no.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        -12.358          -4.387           2.470
summary (lm5)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -42.387 -21.662  -7.825   9.638 114.344 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -12.3577    17.6005  -0.702  0.48647   
## daily.ph.range  -4.3872     3.2948  -1.332  0.19019   
## salinity         2.4703     0.7156   3.452  0.00128 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.93 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2349, Adjusted R-squared:  0.1984 
## F-statistic: 6.446 on 2 and 42 DF,  p-value: 0.003619
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.ph.range  1   1120  1120.3  0.9733 0.329497   
## salinity        1  13718 13717.8 11.9181 0.001281 **
## Residuals      42  48342  1151.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on density: daily median ph

lm6 <- lm(no.fuc.q ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = no.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     -111.620        12.505         2.398
summary (lm6)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.926 -25.042  -9.958  13.099 114.078 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -111.6205   310.1213  -0.360  0.72071   
## daily.med.ph   12.5048    38.9795   0.321  0.74995   
## salinity        2.3980     0.7298   3.286  0.00206 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.59 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2045, Adjusted R-squared:  0.1666 
## F-statistic: 5.399 on 2 and 42 DF,  p-value: 0.008193
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.ph  1      0     0.5  0.0004 0.983854   
## salinity      1  12920 12920.0 10.7967 0.002058 **
## Residuals    42  50260  1196.7                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on density: number of days with a daily minimun ph less than 7

lm7 <- lm(no.fuc.q ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          -9.0117           -0.9642            2.6204
summary (lm7)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -36.89 -24.83 -10.85  17.58 106.13 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -9.0117    17.6826  -0.510 0.612974    
## min.daily.ph.lt7  -0.9642     0.6662  -1.447 0.155226    
## salinity           2.6204     0.7295   3.592 0.000853 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.8 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2404, Adjusted R-squared:  0.2043 
## F-statistic: 6.647 on 2 and 42 DF,  p-value: 0.003103
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                  Df Sum Sq Mean Sq F value    Pr(>F)    
## min.daily.ph.lt7  1    447   447.1  0.3913 0.5350185    
## salinity          1  14744 14743.8 12.9036 0.0008531 ***
## Residuals        42  47990  1142.6                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density: number of days with a daily minimun ph less than 8

lm7 <- lm(no.fuc.q ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##        -10.41267          -0.07862           2.37825
summary (lm7)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.655 -25.355  -9.561  10.160 114.521 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -10.41267   19.71015  -0.528  0.60008   
## min.daily.ph.lt8  -0.07862    0.33874  -0.232  0.81760   
## salinity           2.37825    0.72655   3.273  0.00213 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.61 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2036, Adjusted R-squared:  0.1656 
## F-statistic: 5.368 on 2 and 42 DF,  p-value: 0.008396
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.ph.lt8  1     25    24.9  0.0208 0.885982   
## salinity          1  12837 12836.9 10.7148 0.002131 **
## Residuals        42  50319  1198.1                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density: number of days with a daily maximum ph less than 7

lm10 <- lm(no.fuc.q ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          -9.0117           -0.9642            2.6204
summary (lm10)
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -36.89 -24.83 -10.85  17.58 106.13 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -9.0117    17.6826  -0.510 0.612974    
## max.daily.ph.lt7  -0.9642     0.6662  -1.447 0.155226    
## salinity           2.6204     0.7295   3.592 0.000853 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.8 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2404, Adjusted R-squared:  0.2043 
## F-statistic: 6.647 on 2 and 42 DF,  p-value: 0.003103
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                  Df Sum Sq Mean Sq F value    Pr(>F)    
## max.daily.ph.lt7  1    447   447.1  0.3913 0.5350185    
## salinity          1  14744 14743.8 12.9036 0.0008531 ***
## Residuals        42  47990  1142.6                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect salinity and pH on density: number of days with a daily ph range greater than 0.5

lm13 <- lm(no.fuc.q ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = no.fuc.q ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##               -9.996                -0.572                 2.527
summary (lm13)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -38.61 -24.18  -8.49  13.55 114.27 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)           -9.9962    17.9847  -0.556  0.58129   
## daily.ph.range.gt0.5  -0.5720     0.6396  -0.894  0.37628   
## salinity               2.5272     0.7401   3.415  0.00143 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.31 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2175, Adjusted R-squared:  0.1802 
## F-statistic: 5.835 on 2 and 42 DF,  p-value: 0.005804
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                      Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.ph.range.gt0.5  1     13    13.1  0.0111 0.916469   
## salinity              1  13726 13725.6 11.6597 0.001427 **
## Residuals            42  49442  1177.2                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

####Q1.2 Effect of salinity and pH on percent cover#### Different salinity terms first

Effect of pH and salinity on percent cover

lm1 <- lm(cover ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = cover ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##   -228.6859       6.1732      32.7590      -0.7582
summary (lm1)
## 
## Call:
## lm(formula = cover ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.478 -10.696   2.522   8.911  25.604 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -228.6859   622.7157  -0.367    0.715
## salinity       6.1732    27.6378   0.223    0.824
## ph            32.7590    78.7793   0.416    0.680
## salinity:ph   -0.7582     3.4987  -0.217    0.829
## 
## Residual standard error: 15.06 on 41 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02726,    Adjusted R-squared:  -0.04392 
## F-statistic: 0.3829 on 3 and 41 DF,  p-value: 0.7658
anova (lm1)
## Analysis of Variance Table
## 
## Response: cover
##             Df Sum Sq Mean Sq F value Pr(>F)
## salinity     1   59.2  59.168  0.2609 0.6122
## ph           1  190.7 190.671  0.8409 0.3645
## salinity:ph  1   10.7  10.650  0.0470 0.8295
## Residuals   41 9297.0 226.756
plot (lm1)

Effect of pH and salinity on percent cover, interaction term removed

lm2 <- lm(cover ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = cover ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    -97.1578       0.1838      16.1161
summary (lm2)
## 
## Call:
## lm(formula = cover ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.140 -10.763   2.683   9.241  25.532 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -97.1578   137.8757  -0.705    0.485
## salinity      0.1838     0.3133   0.587    0.560
## ph           16.1161    17.3744   0.928    0.359
## 
## Residual standard error: 14.89 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02614,    Adjusted R-squared:  -0.02023 
## F-statistic: 0.5637 on 2 and 42 DF,  p-value: 0.5734
anova (lm2)
## Analysis of Variance Table
## 
## Response: cover
##           Df Sum Sq Mean Sq F value Pr(>F)
## salinity   1   59.2  59.168  0.2670 0.6081
## ph         1  190.7 190.671  0.8604 0.3589
## Residuals 42 9307.6 221.610
plot (lm2)

Effect and salinity and pH on percent cover: daily minimum salinity

lm3 <- lm(cover ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = cover ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      -97.6669         0.3071        15.8975
summary (lm3)
## 
## Call:
## lm(formula = cover ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.226 -10.112   2.533   9.797  23.934 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -97.6669   135.7036  -0.720    0.476
## daily.min.sal   0.3071     0.2798   1.097    0.279
## ph             15.8975    17.1571   0.927    0.359
## 
## Residual standard error: 14.74 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.04552,    Adjusted R-squared:  7.31e-05 
## F-statistic: 1.002 on 2 and 42 DF,  p-value: 0.3759
anova (lm3)
## Analysis of Variance Table
## 
## Response: cover
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.min.sal  1  248.6  248.62  1.1447 0.2908
## ph             1  186.5  186.48  0.8585 0.3594
## Residuals     42 9122.4  217.20
plot (lm3)

Effect and salinity and pH on percent cover: daily maximum salinity

lm4 <- lm(cover ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = cover ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##    -86.329231      -0.003993      15.308840
summary (lm4)
## 
## Call:
## lm(formula = cover ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.338 -10.610   3.594   8.030  27.016 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -86.329231 139.636447  -0.618    0.540
## daily.max.sal  -0.003993   0.388812  -0.010    0.992
## ph             15.308840  17.502226   0.875    0.387
## 
## Residual standard error: 14.95 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01816,    Adjusted R-squared:  -0.02859 
## F-statistic: 0.3884 on 2 and 42 DF,  p-value: 0.6806
anova (lm4)
## Analysis of Variance Table
## 
## Response: cover
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.max.sal  1    2.6   2.622  0.0117 0.9143
## ph             1  170.9 170.936  0.7651 0.3867
## Residuals     42 9383.9 223.427
plot (lm4)

Effect and salinity and pH on percent cover: daily salinity range

lm5 <- lm(cover ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = cover ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##        -49.3543          -0.9592          11.2057
summary (lm5)
## 
## Call:
## lm(formula = cover ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.663  -7.605   2.940   8.617  22.569 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -49.3543   132.6258  -0.372   0.7117  
## daily.sal.range  -0.9592     0.4852  -1.977   0.0546 .
## ph               11.2057    16.7667   0.668   0.5076  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.3 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1018, Adjusted R-squared:  0.05898 
## F-statistic: 2.379 on 2 and 42 DF,  p-value: 0.105
anova (lm5)
## Analysis of Variance Table
## 
## Response: cover
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1  881.2  881.18  4.3110 0.04402 *
## ph               1   91.3   91.30  0.4467 0.50758  
## Residuals       42 8585.0  204.40                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on percent cover: daily median salinity

lm6 <- lm(cover ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = cover ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      -98.4481         0.2061        16.2167
summary (lm6)
## 
## Call:
## lm(formula = cover ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.171 -10.832   2.538   9.441  25.325 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -98.4481   137.7296  -0.715    0.479
## daily.med.sal   0.2061     0.3126   0.659    0.513
## ph             16.2167    17.3565   0.934    0.355
## 
## Residual standard error: 14.87 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02821,    Adjusted R-squared:  -0.01807 
## F-statistic: 0.6096 on 2 and 42 DF,  p-value: 0.5483
anova (lm6)
## Analysis of Variance Table
## 
## Response: cover
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.med.sal  1   76.5  76.546  0.3461 0.5595
## ph             1  193.0 193.048  0.8730 0.3555
## Residuals     42 9287.9 221.140
plot (lm6)

Effect of salinity and pH on percent cover: number of days with a daily minimun salinity less than 5

lm7 <- lm(cover ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##         -111.8515            -0.1711            18.7576
summary (lm7)
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.338  -9.354   2.462  10.726  26.878 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -111.8515   143.2559  -0.781    0.439
## min.daily.sal.lt5   -0.1711     0.2904  -0.589    0.559
## ph                  18.7576    18.2735   1.026    0.311
## 
## Residual standard error: 14.89 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02621,    Adjusted R-squared:  -0.02017 
## F-statistic: 0.5651 on 2 and 42 DF,  p-value: 0.5726
anova (lm7)
## Analysis of Variance Table
## 
## Response: cover
##                   Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1   17.0  16.962  0.0765 0.7834
## ph                 1  233.5 233.493  1.0537 0.3105
## Residuals         42 9307.0 221.596
plot (lm7)

Effect of salinity and pH on percent cover: number of days with a daily minimun salinity less than 10

lm8 <- lm(cover ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##          -115.9994             -0.1726             19.3339
summary (lm8)
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.799  -9.732   3.215  10.221  26.488 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -115.9994   144.5034  -0.803    0.427
## min.daily.sal.lt10   -0.1726     0.2763  -0.625    0.536
## ph                   19.3339    18.4622   1.047    0.301
## 
## Residual standard error: 14.88 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0272, Adjusted R-squared:  -0.01913 
## F-statistic: 0.5871 on 2 and 42 DF,  p-value: 0.5604
anova (lm8)
## Analysis of Variance Table
## 
## Response: cover
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1   17.2  17.151  0.0775 0.7821
## ph                  1  242.8 242.769  1.0967 0.3010
## Residuals          42 9297.6 221.370
plot (lm8)

Effect of salinity and pH on percent cover: number of days with a daily minimun salinity less than 15

lm9 <- lm(cover ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##           -99.6860             -0.1576             17.2723
summary (lm9)
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.635 -10.029   2.974  10.577  26.311 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -99.6860   138.2439  -0.721    0.475
## min.daily.sal.lt15  -0.1576     0.2536  -0.621    0.538
## ph                  17.2723    17.5944   0.982    0.332
## 
## Residual standard error: 14.88 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0271, Adjusted R-squared:  -0.01923 
## F-statistic: 0.5849 on 2 and 42 DF,  p-value: 0.5616
anova (lm9)
## Analysis of Variance Table
## 
## Response: cover
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1   45.6  45.617  0.2060 0.6522
## ph                  1  213.4 213.360  0.9637 0.3319
## Residuals          42 9298.5 221.393
plot (lm9)

Effect of salinity and pH on percent cover: number of days with a daily maximum salinity less than 5

lm10 <- lm(cover ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##         -139.1476            -0.2888            22.3808
summary (lm10)
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.910  -8.564   1.784   9.850  26.726 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -139.1476   144.7286  -0.961    0.342
## max.daily.sal.lt5   -0.2888     0.2789  -1.035    0.306
## ph                  22.3808    18.4768   1.211    0.233
## 
## Residual standard error: 14.76 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.04259,    Adjusted R-squared:  -0.002997 
## F-statistic: 0.9343 on 2 and 42 DF,  p-value: 0.4009
anova (lm10)
## Analysis of Variance Table
## 
## Response: cover
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1   87.4   87.42  0.4013 0.5299
## ph                 1  319.7  319.66  1.4672 0.2326
## Residuals         42 9150.4  217.87
plot (lm10)

Effect of salinity and pH on percent cover: number of days with a daily maximum salinity less than 10

lm11 <- lm(cover ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##          -137.2208             -0.2759             22.1332
summary (lm11)
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.869  -8.766   1.681   9.801  26.627 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -137.2208   145.3862  -0.944    0.351
## max.daily.sal.lt10   -0.2759     0.2838  -0.972    0.337
## ph                   22.1332    18.5709   1.192    0.240
## 
## Residual standard error: 14.78 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03976,    Adjusted R-squared:  -0.005968 
## F-statistic: 0.8695 on 2 and 42 DF,  p-value: 0.4266
anova (lm11)
## Analysis of Variance Table
## 
## Response: cover
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1   69.6  69.605  0.3185 0.5755
## ph                  1  310.4 310.381  1.4204 0.2400
## Residuals          42 9177.5 218.512
plot (lm11)

Effect of salinity and pH on percent cover: number of days with a daily maximum salinity less than 15

lm12 <- lm(cover ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##          -122.4243             -0.1729             20.1394
summary (lm12)
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.818  -9.665   3.279  10.297  26.552 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -122.4243   148.1750  -0.826    0.413
## max.daily.sal.lt15   -0.1729     0.2768  -0.625    0.536
## ph                   20.1394    18.9488   1.063    0.294
## 
## Residual standard error: 14.88 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02719,    Adjusted R-squared:  -0.01913 
## F-statistic: 0.587 on 2 and 42 DF,  p-value: 0.5605
anova (lm12)
## Analysis of Variance Table
## 
## Response: cover
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1    9.8   9.845  0.0445 0.8340
## ph                  1  250.1 250.064  1.1296 0.2939
## Residuals          42 9297.6 221.371
plot (lm12)

Effect of salinity and pH on percent cover: number of days with a daily salinity range greater than 10

lm13 <- lm(cover ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = cover ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##            -132.1088               -0.2752               21.4750
summary (lm13)
## 
## Call:
## lm(formula = cover ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.716  -8.691   1.084  10.013  26.708 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)          -132.1088   143.4407  -0.921    0.362
## daily.sal.range.gt10   -0.2752     0.2808  -0.980    0.333
## ph                     21.4750    18.3060   1.173    0.247
## 
## Residual standard error: 14.78 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0401, Adjusted R-squared:  -0.005607 
## F-statistic: 0.8773 on 2 and 42 DF,  p-value: 0.4234
anova (lm13)
## Analysis of Variance Table
## 
## Response: cover
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1   82.7  82.668  0.3785 0.5417
## ph                    1  300.6 300.605  1.3762 0.2474
## Residuals            42 9174.2 218.433
plot (lm13)

Effect of salinity and pH on percent cover: number of days with a daily salinity range greater than 5

lm14 <- lm(cover ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = cover ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            -82.3446              -0.3791              15.5111
summary (lm14)
## 
## Call:
## lm(formula = cover ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.632  -9.470   1.619   9.596  25.098 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         -82.3446   133.7847  -0.616    0.542
## daily.sal.range.gt5  -0.3791     0.2540  -1.492    0.143
## ph                   15.5111    16.9504   0.915    0.365
## 
## Residual standard error: 14.57 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.06759,    Adjusted R-squared:  0.02319 
## F-statistic: 1.522 on 2 and 42 DF,  p-value: 0.23
anova (lm14)
## Analysis of Variance Table
## 
## Response: cover
##                     Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1  468.3  468.32  2.2072 0.1448
## ph                   1  177.7  177.67  0.8374 0.3654
## Residuals           42 8911.5  212.18
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent cover: daily minimum ph

lm3 <- lm(cover ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = cover ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##    -173.2523       25.9317        0.2199
summary (lm3)
## 
## Call:
## lm(formula = cover ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.050  -9.531   2.423   8.798  25.536 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -173.2523   185.1600  -0.936    0.355
## daily.min.ph   25.9317    23.5415   1.102    0.277
## salinity        0.2199     0.3156   0.697    0.490
## 
## Residual standard error: 14.83 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0341, Adjusted R-squared:  -0.0119 
## F-statistic: 0.7413 on 2 and 42 DF,  p-value: 0.4826
anova (lm3)
## Analysis of Variance Table
## 
## Response: cover
##              Df Sum Sq Mean Sq F value Pr(>F)
## daily.min.ph  1  219.1  219.14  0.9970 0.3238
## salinity      1  106.7  106.73  0.4856 0.4898
## Residuals    42 9231.6  219.80
plot (lm3)

Effect salinity and pH on percent cover: daily maximum ph

lm4 <- lm(cover ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = cover ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     -72.9611       12.8772        0.1915
summary (lm4)
## 
## Call:
## lm(formula = cover ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.714 -10.848   2.389  10.283  25.393 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -72.9611   100.8935  -0.723    0.474
## daily.max.ph  12.8772    12.5172   1.029    0.309
## salinity       0.1915     0.3130   0.612    0.544
## 
## Residual standard error: 14.85 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03062,    Adjusted R-squared:  -0.01554 
## F-statistic: 0.6633 on 2 and 42 DF,  p-value: 0.5205
anova (lm4)
## Analysis of Variance Table
## 
## Response: cover
##              Df Sum Sq Mean Sq F value Pr(>F)
## daily.max.ph  1  210.1 210.084  0.9524 0.3347
## salinity      1   82.5  82.547  0.3742 0.5440
## Residuals    42 9264.8 220.591
plot (lm4)

Effect of salinity and pH on percent cover: daily ph range

lm5 <- lm(cover ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = cover ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        30.4977         -2.4914          0.2163
summary (lm5)
## 
## Call:
## lm(formula = cover ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.790  -7.752   1.405  10.218  24.666 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     30.4977     7.5265   4.052 0.000215 ***
## daily.ph.range  -2.4914     1.4089  -1.768 0.084271 .  
## salinity         0.2163     0.3060   0.707 0.483608    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.51 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.07505,    Adjusted R-squared:  0.03101 
## F-statistic: 1.704 on 2 and 42 DF,  p-value: 0.1943
anova (lm5)
## Analysis of Variance Table
## 
## Response: cover
##                Df Sum Sq Mean Sq F value Pr(>F)  
## daily.ph.range  1  612.2  612.19  2.9085 0.0955 .
## salinity        1  105.1  105.14  0.4995 0.4836  
## Residuals      42 8840.1  210.48                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on percent cover: daily median ph

lm6 <- lm(cover ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = cover ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##    -124.4823       19.5165        0.1993
summary (lm6)
## 
## Call:
## lm(formula = cover ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.480  -9.664   2.651   9.790  25.503 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -124.4823   132.6707  -0.938    0.353
## daily.med.ph   19.5165    16.6755   1.170    0.248
## salinity        0.1993     0.3122   0.638    0.527
## 
## Residual standard error: 14.8 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03758,    Adjusted R-squared:  -0.008251 
## F-statistic:  0.82 on 2 and 42 DF,  p-value: 0.4474
anova (lm6)
## Analysis of Variance Table
## 
## Response: cover
##              Df Sum Sq Mean Sq F value Pr(>F)
## daily.med.ph  1  269.9  269.89  1.2323 0.2733
## salinity      1   89.3   89.27  0.4076 0.5267
## Residuals    42 9198.3  219.01
plot (lm6)

Effect salinity and pH on percent cover: number of days with a daily minimun ph less than 7

lm7 <- lm(cover ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = cover ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##         30.82111          -0.08506           0.18316
summary (lm7)
## 
## Call:
## lm(formula = cover ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.745  -9.324   2.348  11.137  25.657 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      30.82111    7.85903   3.922 0.000319 ***
## min.daily.ph.lt7 -0.08506    0.29609  -0.287 0.775316    
## salinity          0.18316    0.32421   0.565 0.575118    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.02 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.00814,    Adjusted R-squared:  -0.03909 
## F-statistic: 0.1723 on 2 and 42 DF,  p-value: 0.8423
anova (lm7)
## Analysis of Variance Table
## 
## Response: cover
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1    5.8   5.758  0.0255 0.8739
## salinity          1   72.0  72.037  0.3192 0.5751
## Residuals        42 9479.7 225.707
plot (lm7)

Effect salinity and pH on percent cover: number of days with a daily minimun ph less than 8

lm7 <- lm(cover ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = cover ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##          25.1075            0.2261            0.1484
summary (lm7)
## 
## Call:
## lm(formula = cover ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.356  -9.074   4.115   9.784  25.374 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       25.1075     8.3195   3.018  0.00431 **
## min.daily.ph.lt8   0.2261     0.1430   1.581  0.12137   
## salinity           0.1484     0.3067   0.484  0.63099   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.61 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.06202,    Adjusted R-squared:  0.01735 
## F-statistic: 1.388 on 2 and 42 DF,  p-value: 0.2607
anova (lm7)
## Analysis of Variance Table
## 
## Response: cover
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt8  1  542.7  542.75  2.5428 0.1183
## salinity          1   50.0   49.97  0.2341 0.6310
## Residuals        42 8964.7  213.45
plot (lm7)

Effect salinity and pH on percent cover: number of days with a daily maximum ph less than 7

lm10 <- lm(cover ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = cover ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##         30.82111          -0.08506           0.18316
summary (lm10)
## 
## Call:
## lm(formula = cover ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.745  -9.324   2.348  11.137  25.657 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      30.82111    7.85903   3.922 0.000319 ***
## max.daily.ph.lt7 -0.08506    0.29609  -0.287 0.775316    
## salinity          0.18316    0.32421   0.565 0.575118    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.02 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.00814,    Adjusted R-squared:  -0.03909 
## F-statistic: 0.1723 on 2 and 42 DF,  p-value: 0.8423
anova (lm10)
## Analysis of Variance Table
## 
## Response: cover
##                  Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1    5.8   5.758  0.0255 0.8739
## salinity          1   72.0  72.037  0.3192 0.5751
## Residuals        42 9479.7 225.707
plot (lm10)

Effect salinity and pH on percent cover: number of days with a daily ph range greater than 0.5

lm13 <- lm(cover ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = cover ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##            30.500259              0.007668              0.159347
summary (lm13)
## 
## Call:
## lm(formula = cover ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -28.18 -10.60   2.24  10.84  25.90 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          30.500259   7.882725   3.869 0.000374 ***
## daily.ph.range.gt0.5  0.007668   0.280358   0.027 0.978310    
## salinity              0.159347   0.324385   0.491 0.625823    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.04 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.006208,   Adjusted R-squared:  -0.04111 
## F-statistic: 0.1312 on 2 and 42 DF,  p-value: 0.8774
anova (lm13)
## Analysis of Variance Table
## 
## Response: cover
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1    4.8   4.767  0.0211 0.8853
## salinity              1   54.6  54.570  0.2413 0.6258
## Residuals            42 9498.1 226.146
plot (lm13)

####Q1.3 Effect of salinity and pH on density of large thalli#### Different salinity terms first

Effect of pH and salinity on density of small thalli

lm1 <- lm(no.small.fuc.q ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##     591.555      -34.246      -77.736        4.661
summary (lm1)
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.95 -22.58 -11.00  11.88 104.52 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  591.555   1473.667   0.401    0.690
## salinity     -34.246     66.946  -0.512    0.612
## ph           -77.736    186.482  -0.417    0.679
## salinity:ph    4.661      8.480   0.550    0.586
## 
## Residual standard error: 33.83 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.251,  Adjusted R-squared:  0.1919 
## F-statistic: 4.245 on 3 and 38 DF,  p-value: 0.01108
anova (lm1)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity     1  13947 13946.9 12.1831 0.001238 **
## ph           1    287   287.0  0.2507 0.619484   
## salinity:ph  1    346   345.8  0.3021 0.585788   
## Residuals   38  43501  1144.8                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on density of small thalli, interaction term removed

lm2 <- lm(no.small.fuc.q ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    -195.539        2.548       21.900
summary (lm2)
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.181 -23.278  -5.598  11.485 105.143 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -195.5391   344.6575  -0.567    0.574   
## salinity       2.5480     0.7162   3.557    0.001 **
## ph            21.9004    43.3482   0.505    0.616   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.53 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2451, Adjusted R-squared:  0.2064 
## F-statistic:  6.33 on 2 and 39 DF,  p-value: 0.004161
anova (lm2)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity   1  13947 13946.9 12.4051 0.001109 **
## ph         1    287   287.0  0.2552 0.616247   
## Residuals 39  43847  1124.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on density of small thalli: daily minimum salinity

lm3 <- lm(no.small.fuc.q ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      -132.052          2.357         15.094
summary (lm3)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.52 -24.29  -5.16  13.18 102.70 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -132.0517   338.8900  -0.390  0.69891    
## daily.min.sal    2.3569     0.6393   3.687  0.00069 ***
## ph              15.0937    42.7636   0.353  0.72602    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.23 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2585, Adjusted R-squared:  0.2205 
## F-statistic: 6.798 on 2 and 39 DF,  p-value: 0.002932
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.min.sal  1  14877 14876.7 13.4719 0.0007234 ***
## ph             1    138   137.6  0.1246 0.7260217    
## Residuals     39  43067  1104.3                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on density of small thalli: daily maximum salinity

lm4 <- lm(no.small.fuc.q ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      -223.882          2.807         23.699
summary (lm4)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.55 -25.77 -10.17  11.76 106.30 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -223.8822   359.1727  -0.623  0.53670   
## daily.max.sal    2.8072     0.9145   3.070  0.00389 **
## ph              23.6990    44.9415   0.527  0.60095   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.63 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1947, Adjusted R-squared:  0.1534 
## F-statistic: 4.714 on 2 and 39 DF,  p-value: 0.01466
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.sal  1  10974 10974.5  9.1507 0.004384 **
## ph             1    333   333.5  0.2781 0.600952   
## Residuals     39  46773  1199.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on density of small thalli: daily salinity range

lm5 <- lm(no.small.fuc.q ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##          97.517           -2.606           -6.007
summary (lm5)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.402 -26.647  -7.667  10.943 106.768 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       97.517    373.086   0.261   0.7952  
## daily.sal.range   -2.606      1.254  -2.079   0.0443 *
## ph                -6.007     47.182  -0.127   0.8993  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.61 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09982,    Adjusted R-squared:  0.05366 
## F-statistic: 2.162 on 2 and 39 DF,  p-value: 0.1286
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1   5776  5776.1  4.3086 0.04456 *
## ph               1     22    21.7  0.0162 0.89935  
## Residuals       39  52283  1340.6                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on density of small thalli: daily median salinity

lm6 <- lm(no.small.fuc.q ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      -192.276          2.527         21.598
summary (lm6)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.612 -22.907  -5.843  11.544 105.248 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -192.2764   345.3734  -0.557   0.5809   
## daily.med.sal    2.5270     0.7172   3.524   0.0011 **
## ph              21.5982    43.4432   0.497   0.6219   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.61 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2416, Adjusted R-squared:  0.2027 
## F-statistic: 6.211 on 2 and 39 DF,  p-value: 0.004555
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.sal  1  13751 13750.7 12.1739 0.001218 **
## ph             1    279   279.2  0.2472 0.621867   
## Residuals     39  44051  1129.5                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 5

lm7 <- lm(no.small.fuc.q ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          23.48801            0.09737            1.60765
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.047 -24.145 -19.666   9.549 111.814 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        23.48801  399.39526   0.059    0.953
## min.daily.sal.lt5   0.09737    0.77100   0.126    0.900
## ph                  1.60765   50.81535   0.032    0.975
## 
## Residual standard error: 38.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0005065,  Adjusted R-squared:  -0.05075 
## F-statistic: 0.009881 on 2 and 39 DF,  p-value: 0.9902
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1     28   27.93  0.0188 0.8918
## ph                 1      1    1.49  0.0010 0.9749
## Residuals         39  58052 1488.50
plot (lm7)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 10

lm8 <- lm(no.small.fuc.q ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##            -99.780              -0.862              18.758
summary (lm8)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -36.52 -25.02 -16.69  18.13 100.56 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -99.7797   395.1073  -0.253    0.802
## min.daily.sal.lt10  -0.8620     0.7164  -1.203    0.236
## ph                  18.7578    50.3409   0.373    0.711
## 
## Residual standard error: 37.89 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03589,    Adjusted R-squared:  -0.01356 
## F-statistic: 0.7258 on 2 and 39 DF,  p-value: 0.4904
anova (lm8)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1   1885 1884.89  1.3128 0.2589
## ph                  1    199  199.35  0.1388 0.7115
## Residuals          39  55997 1435.81
plot (lm8)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 15

lm9 <- lm(no.small.fuc.q ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##            -14.641              -1.246               8.853
summary (lm9)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -42.05 -25.54 -13.87  19.36  94.05 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        -14.6413   373.9772  -0.039   0.9690  
## min.daily.sal.lt15  -1.2459     0.6506  -1.915   0.0629 .
## ph                   8.8526    47.4353   0.187   0.8529  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.89 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08603,    Adjusted R-squared:  0.03916 
## F-statistic: 1.836 on 2 and 39 DF,  p-value: 0.173
anova (lm9)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt15  1   4950  4949.6  3.6364 0.06392 .
## ph                  1     47    47.4  0.0348 0.85292  
## Residuals          39  53084  1361.1                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on percent no.small.fuc.q: number of days with a daily maximum salinity less than 5

lm10 <- lm(no.small.fuc.q ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##           29.5327             0.1109             0.8209
summary (lm10)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.811 -24.088 -19.610   9.479 111.971 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        29.5327   405.9512   0.073    0.942
## max.daily.sal.lt5   0.1109     0.7411   0.150    0.882
## ph                  0.8209    51.7059   0.016    0.987
## 
## Residual standard error: 38.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0006711,  Adjusted R-squared:  -0.05058 
## F-statistic: 0.0131 on 2 and 39 DF,  p-value: 0.987
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1     39   38.61  0.0259 0.8729
## ph                 1      0    0.38  0.0003 0.9874
## Residuals         39  58042 1488.26
plot (lm10)

Effect of salinity and pH on density of small thalli: number of days with a daily maximum salinity less than 10

lm11 <- lm(no.small.fuc.q ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##            0.56153            -0.08363             4.75928
summary (lm11)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35.15 -25.27 -18.95  11.37 110.02 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.56153  406.72997   0.001    0.999
## max.daily.sal.lt10  -0.08363    0.75313  -0.111    0.912
## ph                   4.75928   51.82753   0.092    0.927
## 
## Residual standard error: 38.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0004137,  Adjusted R-squared:  -0.05085 
## F-statistic: 0.008071 on 2 and 39 DF,  p-value: 0.992
anova (lm11)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1     11   11.48  0.0077 0.9305
## ph                  1     13   12.55  0.0084 0.9273
## Residuals          39  58057 1488.64
plot (lm11)

Effect of salinity and pH on density of small thalli: number of days with a daily maximum salinity less than 15

lm12 <- lm(no.small.fuc.q ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##          -104.4956             -0.6662             18.9982
summary (lm12)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37.92 -25.12 -16.79  16.10 103.18 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -104.4956   406.9142  -0.257    0.799
## max.daily.sal.lt15   -0.6662     0.7188  -0.927    0.360
## ph                   18.9982    51.9103   0.366    0.716
## 
## Residual standard error: 38.17 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.02165,    Adjusted R-squared:  -0.02853 
## F-statistic: 0.4314 on 2 and 39 DF,  p-value: 0.6526
anova (lm12)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1   1062 1062.10  0.7290 0.3984
## ph                  1    195  195.16  0.1339 0.7164
## Residuals          39  56824 1457.02
plot (lm12)

Effect of salinity and pH on density of small thalli: number of days with a daily salinity range greater than 10

lm13 <- lm(no.small.fuc.q ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             21.38062               0.06487               1.91485
summary (lm13)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33.36 -24.40 -19.62   9.93 111.53 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)           21.38062  402.37896   0.053    0.958
## daily.sal.range.gt10   0.06487    0.74733   0.087    0.931
## ph                     1.91485   51.22591   0.037    0.970
## 
## Residual standard error: 38.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0002908,  Adjusted R-squared:  -0.05098 
## F-statistic: 0.005672 on 2 and 39 DF,  p-value: 0.9943
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1     15   14.81  0.0099 0.9211
## ph                    1      2    2.08  0.0014 0.9704
## Residuals            39  58064 1488.82
plot (lm13)

Effect of salinity and pH on density of small thalli: number of days with a daily salinity range greater than 5

lm14 <- lm(no.small.fuc.q ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##              46.668               -1.176                1.020
summary (lm14)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35.27 -26.87 -15.15  20.22  94.55 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          46.6678   376.5033   0.124   0.9020  
## daily.sal.range.gt5  -1.1755     0.6634  -1.772   0.0842 .
## ph                    1.0199    47.6474   0.021   0.9830  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37.12 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07461,    Adjusted R-squared:  0.02715 
## F-statistic: 1.572 on 2 and 39 DF,  p-value: 0.2205
anova (lm14)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                     Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt5  1   4333  4332.7  3.1439 0.08402 .
## ph                   1      1     0.6  0.0005 0.98303  
## Residuals           39  53748  1378.1                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent density of small thalli: daily minimum ph

lm3 <- lm(no.small.fuc.q ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##      133.781       -19.763         2.458
summary (lm3)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.39 -24.09  -7.07  12.03 104.30 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  133.7814   434.9442   0.308  0.76004   
## daily.min.ph -19.7632    55.2639  -0.358  0.72256   
## salinity       2.4583     0.7233   3.399  0.00157 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2426, Adjusted R-squared:  0.2038 
## F-statistic: 6.246 on 2 and 39 DF,  p-value: 0.004433
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.min.ph  1   1060  1060.0  0.9398 0.338309   
## salinity      1  13031 13031.1 11.5529 0.001572 **
## Residuals    39  43990  1127.9                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on density of small thalli: daily maximum ph

lm4 <- lm(no.small.fuc.q ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     -283.171        32.504         2.601
summary (lm4)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -43.49 -23.25  -9.67  11.66  96.26 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -283.1711   235.3741  -1.203  0.23620    
## daily.max.ph   32.5037    29.1734   1.114  0.27203    
## salinity        2.6015     0.7076   3.677  0.00071 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.12 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2636, Adjusted R-squared:  0.2258 
## F-statistic: 6.979 on 2 and 39 DF,  p-value: 0.002565
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.max.ph  1    483   482.8  0.4403 0.5109011    
## salinity      1  14825 14825.4 13.5178 0.0007104 ***
## Residuals    39  42773  1096.7                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on density of small thalli: daily ph range

lm5 <- lm(no.small.fuc.q ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        -21.718          -3.802           2.592
summary (lm5)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.889 -22.940  -3.613  10.334 104.912 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -21.7182    17.2378  -1.260 0.215188    
## daily.ph.range  -3.8016     3.2149  -1.183 0.244163    
## salinity         2.5921     0.7047   3.678 0.000707 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.05 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2664, Adjusted R-squared:  0.2288 
## F-statistic: 7.082 on 2 and 39 DF,  p-value: 0.002377
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.ph.range  1    694   693.9  0.6352 0.4302816    
## salinity        1  14781 14780.6 13.5294 0.0007071 ***
## Residuals      39  42607  1092.5                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on density of small thalli: daily median ph

lm6 <- lm(no.small.fuc.q ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     -145.404        15.564         2.541
summary (lm6)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -40.21 -23.82  -5.84  11.52 104.47 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -145.4045   312.2875  -0.466  0.64408   
## daily.med.ph   15.5639    39.2082   0.397  0.69356   
## salinity        2.5410     0.7179   3.539  0.00106 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.57 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2432, Adjusted R-squared:  0.2044 
## F-statistic: 6.266 on 2 and 39 DF,  p-value: 0.004368
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.ph  1      5     5.0  0.0045 0.947086   
## salinity      1  14119 14119.4 12.5273 0.001055 **
## Residuals    39  43957  1127.1                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on density of small thalli: number of days with a daily minimun ph less than 7

lm7 <- lm(no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##         -18.0806           -0.9547            2.7443
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.046 -25.895  -7.856  14.993  96.607 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -18.0806    17.2608  -1.047 0.301321    
## min.daily.ph.lt7  -0.9547     0.6565  -1.454 0.153889    
## salinity           2.7443     0.7140   3.843 0.000436 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.76 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2792, Adjusted R-squared:  0.2422 
## F-statistic: 7.554 on 2 and 39 DF,  p-value: 0.001688
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value    Pr(>F)    
## min.daily.ph.lt7  1    361   360.7  0.3361 0.5654482    
## salinity          1  15856 15856.1 14.7714 0.0004361 ***
## Residuals        39  41864  1073.4                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density of small thalli: number of days with a daily minimun ph less than 8

lm7 <- lm(no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##        -19.98991          -0.06972           2.50940
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.321 -24.424  -5.949  11.021 105.132 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -19.98991   19.24252  -1.039  0.30528   
## min.daily.ph.lt8  -0.06972    0.33588  -0.208  0.83665   
## salinity           2.50940    0.71332   3.518  0.00112 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.62 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.241,  Adjusted R-squared:  0.202 
## F-statistic: 6.191 on 2 and 39 DF,  p-value: 0.004625
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.ph.lt8  1      6     6.1  0.0054 0.941759   
## salinity          1  13989 13989.4 12.3757 0.001122 **
## Residuals        39  44085  1130.4                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density of small thalli: number of days with a daily maximum ph less than 7

lm10 <- lm(no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##         -18.0806           -0.9547            2.7443
summary (lm10)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.046 -25.895  -7.856  14.993  96.607 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -18.0806    17.2608  -1.047 0.301321    
## max.daily.ph.lt7  -0.9547     0.6565  -1.454 0.153889    
## salinity           2.7443     0.7140   3.843 0.000436 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.76 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2792, Adjusted R-squared:  0.2422 
## F-statistic: 7.554 on 2 and 39 DF,  p-value: 0.001688
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value    Pr(>F)    
## max.daily.ph.lt7  1    361   360.7  0.3361 0.5654482    
## salinity          1  15856 15856.1 14.7714 0.0004361 ***
## Residuals        39  41864  1073.4                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect salinity and pH on density of small thalli: number of days with a daily ph range greater than 0.5

lm13 <- lm(no.small.fuc.q ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##             -19.2967               -0.5207                2.6348
summary (lm13)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.448 -24.466  -7.248  13.176 104.889 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -19.2967    17.6294  -1.095 0.280417    
## daily.ph.range.gt0.5  -0.5207     0.6371  -0.817 0.418742    
## salinity               2.6348     0.7251   3.634 0.000805 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.36 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2529, Adjusted R-squared:  0.2146 
## F-statistic: 6.602 on 2 and 39 DF,  p-value: 0.003393
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                      Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.ph.range.gt0.5  1      0     0.2  0.0002 0.9887731    
## salinity              1  14690 14689.8 13.2032 0.0008047 ***
## Residuals            39  43391  1112.6                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

####Q1.4 Effect of salinity and pH on desnity of small thalli#### Different salinity terms first

Effect of pH and salinity on density of small thalli

lm1 <- lm(no.small.fuc.q ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##     591.555      -34.246      -77.736        4.661
summary (lm1)
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.95 -22.58 -11.00  11.88 104.52 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  591.555   1473.667   0.401    0.690
## salinity     -34.246     66.946  -0.512    0.612
## ph           -77.736    186.482  -0.417    0.679
## salinity:ph    4.661      8.480   0.550    0.586
## 
## Residual standard error: 33.83 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.251,  Adjusted R-squared:  0.1919 
## F-statistic: 4.245 on 3 and 38 DF,  p-value: 0.01108
anova (lm1)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity     1  13947 13946.9 12.1831 0.001238 **
## ph           1    287   287.0  0.2507 0.619484   
## salinity:ph  1    346   345.8  0.3021 0.585788   
## Residuals   38  43501  1144.8                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on density of small thalli, interaction term removed

lm2 <- lm(no.small.fuc.q ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    -195.539        2.548       21.900
summary (lm2)
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.181 -23.278  -5.598  11.485 105.143 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -195.5391   344.6575  -0.567    0.574   
## salinity       2.5480     0.7162   3.557    0.001 **
## ph            21.9004    43.3482   0.505    0.616   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.53 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2451, Adjusted R-squared:  0.2064 
## F-statistic:  6.33 on 2 and 39 DF,  p-value: 0.004161
anova (lm2)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity   1  13947 13946.9 12.4051 0.001109 **
## ph         1    287   287.0  0.2552 0.616247   
## Residuals 39  43847  1124.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on density of small thalli: daily minimum salinity

lm3 <- lm(no.small.fuc.q ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      -132.052          2.357         15.094
summary (lm3)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.52 -24.29  -5.16  13.18 102.70 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -132.0517   338.8900  -0.390  0.69891    
## daily.min.sal    2.3569     0.6393   3.687  0.00069 ***
## ph              15.0937    42.7636   0.353  0.72602    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.23 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2585, Adjusted R-squared:  0.2205 
## F-statistic: 6.798 on 2 and 39 DF,  p-value: 0.002932
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.min.sal  1  14877 14876.7 13.4719 0.0007234 ***
## ph             1    138   137.6  0.1246 0.7260217    
## Residuals     39  43067  1104.3                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on density of small thalli: daily maximum salinity

lm4 <- lm(no.small.fuc.q ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      -223.882          2.807         23.699
summary (lm4)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.55 -25.77 -10.17  11.76 106.30 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -223.8822   359.1727  -0.623  0.53670   
## daily.max.sal    2.8072     0.9145   3.070  0.00389 **
## ph              23.6990    44.9415   0.527  0.60095   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.63 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1947, Adjusted R-squared:  0.1534 
## F-statistic: 4.714 on 2 and 39 DF,  p-value: 0.01466
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.sal  1  10974 10974.5  9.1507 0.004384 **
## ph             1    333   333.5  0.2781 0.600952   
## Residuals     39  46773  1199.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on density of small thalli: daily salinity range

lm5 <- lm(no.small.fuc.q ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##          97.517           -2.606           -6.007
summary (lm5)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.402 -26.647  -7.667  10.943 106.768 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       97.517    373.086   0.261   0.7952  
## daily.sal.range   -2.606      1.254  -2.079   0.0443 *
## ph                -6.007     47.182  -0.127   0.8993  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.61 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09982,    Adjusted R-squared:  0.05366 
## F-statistic: 2.162 on 2 and 39 DF,  p-value: 0.1286
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1   5776  5776.1  4.3086 0.04456 *
## ph               1     22    21.7  0.0162 0.89935  
## Residuals       39  52283  1340.6                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on density of small thalli: daily median salinity

lm6 <- lm(no.small.fuc.q ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      -192.276          2.527         21.598
summary (lm6)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.612 -22.907  -5.843  11.544 105.248 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -192.2764   345.3734  -0.557   0.5809   
## daily.med.sal    2.5270     0.7172   3.524   0.0011 **
## ph              21.5982    43.4432   0.497   0.6219   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.61 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2416, Adjusted R-squared:  0.2027 
## F-statistic: 6.211 on 2 and 39 DF,  p-value: 0.004555
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.sal  1  13751 13750.7 12.1739 0.001218 **
## ph             1    279   279.2  0.2472 0.621867   
## Residuals     39  44051  1129.5                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 5

lm7 <- lm(no.small.fuc.q ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          23.48801            0.09737            1.60765
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.047 -24.145 -19.666   9.549 111.814 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        23.48801  399.39526   0.059    0.953
## min.daily.sal.lt5   0.09737    0.77100   0.126    0.900
## ph                  1.60765   50.81535   0.032    0.975
## 
## Residual standard error: 38.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0005065,  Adjusted R-squared:  -0.05075 
## F-statistic: 0.009881 on 2 and 39 DF,  p-value: 0.9902
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1     28   27.93  0.0188 0.8918
## ph                 1      1    1.49  0.0010 0.9749
## Residuals         39  58052 1488.50
plot (lm7)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 10

lm8 <- lm(no.small.fuc.q ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##            -99.780              -0.862              18.758
summary (lm8)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -36.52 -25.02 -16.69  18.13 100.56 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -99.7797   395.1073  -0.253    0.802
## min.daily.sal.lt10  -0.8620     0.7164  -1.203    0.236
## ph                  18.7578    50.3409   0.373    0.711
## 
## Residual standard error: 37.89 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03589,    Adjusted R-squared:  -0.01356 
## F-statistic: 0.7258 on 2 and 39 DF,  p-value: 0.4904
anova (lm8)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1   1885 1884.89  1.3128 0.2589
## ph                  1    199  199.35  0.1388 0.7115
## Residuals          39  55997 1435.81
plot (lm8)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 15

lm9 <- lm(no.small.fuc.q ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##            -14.641              -1.246               8.853
summary (lm9)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -42.05 -25.54 -13.87  19.36  94.05 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        -14.6413   373.9772  -0.039   0.9690  
## min.daily.sal.lt15  -1.2459     0.6506  -1.915   0.0629 .
## ph                   8.8526    47.4353   0.187   0.8529  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.89 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08603,    Adjusted R-squared:  0.03916 
## F-statistic: 1.836 on 2 and 39 DF,  p-value: 0.173
anova (lm9)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt15  1   4950  4949.6  3.6364 0.06392 .
## ph                  1     47    47.4  0.0348 0.85292  
## Residuals          39  53084  1361.1                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on percent no.small.fuc.q: number of days with a daily maximum salinity less than 5

lm10 <- lm(no.small.fuc.q ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##           29.5327             0.1109             0.8209
summary (lm10)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.811 -24.088 -19.610   9.479 111.971 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        29.5327   405.9512   0.073    0.942
## max.daily.sal.lt5   0.1109     0.7411   0.150    0.882
## ph                  0.8209    51.7059   0.016    0.987
## 
## Residual standard error: 38.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0006711,  Adjusted R-squared:  -0.05058 
## F-statistic: 0.0131 on 2 and 39 DF,  p-value: 0.987
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1     39   38.61  0.0259 0.8729
## ph                 1      0    0.38  0.0003 0.9874
## Residuals         39  58042 1488.26
plot (lm10)

Effect of salinity and pH on density of small thalli: number of days with a daily maximum salinity less than 10

lm11 <- lm(no.small.fuc.q ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##            0.56153            -0.08363             4.75928
summary (lm11)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35.15 -25.27 -18.95  11.37 110.02 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.56153  406.72997   0.001    0.999
## max.daily.sal.lt10  -0.08363    0.75313  -0.111    0.912
## ph                   4.75928   51.82753   0.092    0.927
## 
## Residual standard error: 38.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0004137,  Adjusted R-squared:  -0.05085 
## F-statistic: 0.008071 on 2 and 39 DF,  p-value: 0.992
anova (lm11)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1     11   11.48  0.0077 0.9305
## ph                  1     13   12.55  0.0084 0.9273
## Residuals          39  58057 1488.64
plot (lm11)

Effect of salinity and pH on density of small thalli: number of days with a daily maximum salinity less than 15

lm12 <- lm(no.small.fuc.q ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##          -104.4956             -0.6662             18.9982
summary (lm12)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37.92 -25.12 -16.79  16.10 103.18 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -104.4956   406.9142  -0.257    0.799
## max.daily.sal.lt15   -0.6662     0.7188  -0.927    0.360
## ph                   18.9982    51.9103   0.366    0.716
## 
## Residual standard error: 38.17 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.02165,    Adjusted R-squared:  -0.02853 
## F-statistic: 0.4314 on 2 and 39 DF,  p-value: 0.6526
anova (lm12)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1   1062 1062.10  0.7290 0.3984
## ph                  1    195  195.16  0.1339 0.7164
## Residuals          39  56824 1457.02
plot (lm12)

Effect of salinity and pH on density of small thalli: number of days with a daily salinity range greater than 10

lm13 <- lm(no.small.fuc.q ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             21.38062               0.06487               1.91485
summary (lm13)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33.36 -24.40 -19.62   9.93 111.53 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)           21.38062  402.37896   0.053    0.958
## daily.sal.range.gt10   0.06487    0.74733   0.087    0.931
## ph                     1.91485   51.22591   0.037    0.970
## 
## Residual standard error: 38.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0002908,  Adjusted R-squared:  -0.05098 
## F-statistic: 0.005672 on 2 and 39 DF,  p-value: 0.9943
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1     15   14.81  0.0099 0.9211
## ph                    1      2    2.08  0.0014 0.9704
## Residuals            39  58064 1488.82
plot (lm13)

Effect of salinity and pH on density of small thalli: number of days with a daily salinity range greater than 5

lm14 <- lm(no.small.fuc.q ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##              46.668               -1.176                1.020
summary (lm14)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35.27 -26.87 -15.15  20.22  94.55 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          46.6678   376.5033   0.124   0.9020  
## daily.sal.range.gt5  -1.1755     0.6634  -1.772   0.0842 .
## ph                    1.0199    47.6474   0.021   0.9830  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37.12 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07461,    Adjusted R-squared:  0.02715 
## F-statistic: 1.572 on 2 and 39 DF,  p-value: 0.2205
anova (lm14)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                     Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt5  1   4333  4332.7  3.1439 0.08402 .
## ph                   1      1     0.6  0.0005 0.98303  
## Residuals           39  53748  1378.1                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent density of small thalli: daily minimum ph

lm3 <- lm(no.small.fuc.q ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##      133.781       -19.763         2.458
summary (lm3)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.39 -24.09  -7.07  12.03 104.30 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  133.7814   434.9442   0.308  0.76004   
## daily.min.ph -19.7632    55.2639  -0.358  0.72256   
## salinity       2.4583     0.7233   3.399  0.00157 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2426, Adjusted R-squared:  0.2038 
## F-statistic: 6.246 on 2 and 39 DF,  p-value: 0.004433
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.min.ph  1   1060  1060.0  0.9398 0.338309   
## salinity      1  13031 13031.1 11.5529 0.001572 **
## Residuals    39  43990  1127.9                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on density of small thalli: daily maximum ph

lm4 <- lm(no.small.fuc.q ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     -283.171        32.504         2.601
summary (lm4)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -43.49 -23.25  -9.67  11.66  96.26 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -283.1711   235.3741  -1.203  0.23620    
## daily.max.ph   32.5037    29.1734   1.114  0.27203    
## salinity        2.6015     0.7076   3.677  0.00071 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.12 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2636, Adjusted R-squared:  0.2258 
## F-statistic: 6.979 on 2 and 39 DF,  p-value: 0.002565
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.max.ph  1    483   482.8  0.4403 0.5109011    
## salinity      1  14825 14825.4 13.5178 0.0007104 ***
## Residuals    39  42773  1096.7                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on density of small thalli: daily ph range

lm5 <- lm(no.small.fuc.q ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        -21.718          -3.802           2.592
summary (lm5)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.889 -22.940  -3.613  10.334 104.912 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -21.7182    17.2378  -1.260 0.215188    
## daily.ph.range  -3.8016     3.2149  -1.183 0.244163    
## salinity         2.5921     0.7047   3.678 0.000707 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.05 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2664, Adjusted R-squared:  0.2288 
## F-statistic: 7.082 on 2 and 39 DF,  p-value: 0.002377
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.ph.range  1    694   693.9  0.6352 0.4302816    
## salinity        1  14781 14780.6 13.5294 0.0007071 ***
## Residuals      39  42607  1092.5                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on density of small thalli: daily median ph

lm6 <- lm(no.small.fuc.q ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     -145.404        15.564         2.541
summary (lm6)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -40.21 -23.82  -5.84  11.52 104.47 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -145.4045   312.2875  -0.466  0.64408   
## daily.med.ph   15.5639    39.2082   0.397  0.69356   
## salinity        2.5410     0.7179   3.539  0.00106 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.57 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2432, Adjusted R-squared:  0.2044 
## F-statistic: 6.266 on 2 and 39 DF,  p-value: 0.004368
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.ph  1      5     5.0  0.0045 0.947086   
## salinity      1  14119 14119.4 12.5273 0.001055 **
## Residuals    39  43957  1127.1                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on density of small thalli: number of days with a daily minimun ph less than 7

lm7 <- lm(no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##         -18.0806           -0.9547            2.7443
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.046 -25.895  -7.856  14.993  96.607 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -18.0806    17.2608  -1.047 0.301321    
## min.daily.ph.lt7  -0.9547     0.6565  -1.454 0.153889    
## salinity           2.7443     0.7140   3.843 0.000436 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.76 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2792, Adjusted R-squared:  0.2422 
## F-statistic: 7.554 on 2 and 39 DF,  p-value: 0.001688
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value    Pr(>F)    
## min.daily.ph.lt7  1    361   360.7  0.3361 0.5654482    
## salinity          1  15856 15856.1 14.7714 0.0004361 ***
## Residuals        39  41864  1073.4                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density of small thalli: number of days with a daily minimun ph less than 8

lm7 <- lm(no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##        -19.98991          -0.06972           2.50940
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.321 -24.424  -5.949  11.021 105.132 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -19.98991   19.24252  -1.039  0.30528   
## min.daily.ph.lt8  -0.06972    0.33588  -0.208  0.83665   
## salinity           2.50940    0.71332   3.518  0.00112 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.62 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.241,  Adjusted R-squared:  0.202 
## F-statistic: 6.191 on 2 and 39 DF,  p-value: 0.004625
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.ph.lt8  1      6     6.1  0.0054 0.941759   
## salinity          1  13989 13989.4 12.3757 0.001122 **
## Residuals        39  44085  1130.4                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density of small thalli: number of days with a daily maximum ph less than 7

lm10 <- lm(no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##         -18.0806           -0.9547            2.7443
summary (lm10)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.046 -25.895  -7.856  14.993  96.607 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -18.0806    17.2608  -1.047 0.301321    
## max.daily.ph.lt7  -0.9547     0.6565  -1.454 0.153889    
## salinity           2.7443     0.7140   3.843 0.000436 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.76 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2792, Adjusted R-squared:  0.2422 
## F-statistic: 7.554 on 2 and 39 DF,  p-value: 0.001688
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value    Pr(>F)    
## max.daily.ph.lt7  1    361   360.7  0.3361 0.5654482    
## salinity          1  15856 15856.1 14.7714 0.0004361 ***
## Residuals        39  41864  1073.4                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect salinity and pH on density of small thalli: number of days with a daily ph range greater than 0.5

lm13 <- lm(no.small.fuc.q ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##             -19.2967               -0.5207                2.6348
summary (lm13)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.448 -24.466  -7.248  13.176 104.889 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -19.2967    17.6294  -1.095 0.280417    
## daily.ph.range.gt0.5  -0.5207     0.6371  -0.817 0.418742    
## salinity               2.6348     0.7251   3.634 0.000805 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.36 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2529, Adjusted R-squared:  0.2146 
## F-statistic: 6.602 on 2 and 39 DF,  p-value: 0.003393
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                      Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.ph.range.gt0.5  1      0     0.2  0.0002 0.9887731    
## salinity              1  14690 14689.8 13.2032 0.0008047 ***
## Residuals            39  43391  1112.6                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

####Q2. Effects of salinity and pH on reproductive effort#### Data I have for reproductive effort: covcl.repro (cover class of reproductive tissue), dw.repro (dry weight of reproductive tissue), apices.repro (number of reproductive apices), perc.ra (percent of apices that are reproductive), avg.oog (average number of oogonia) and perc.rdw (percent of dry weight that is reproductive tissue)

Data for salinity: salinity (median salinity), daily.min.sal (daily minimun salinity), daily.max.sal (daily maximum salinity), daily.sal.range (daily salinity range), daily.med.sal (daily median salinity), min.daily.sal.lt5/10/15 (number of days with a minimun daily dalinity less than 5, 10, and 15), max.daily.sal.lt5/10/15 (number of days with a maximum daily salinity less than 5, 10, and 15) and daily.sal.range.gt5/10 (number of days with a daily salinity range greater than 5 and 10)

Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5) ####Q2.1 Effects of salinity and pH on cover class of reproductive tissue#### Different salinity terms first

Effect of pH and salinity on cover class of reproductive tissue

lm1 <- lm(covcl.repro ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = covcl.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##     -8.7659       0.8215       1.5278      -0.1099
summary (lm1)
## 
## Call:
## lm(formula = covcl.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.51736 -0.82441  0.06643  0.69227  2.33210 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -8.7659    40.5168  -0.216    0.830
## salinity      0.8215     1.7982   0.457    0.650
## ph            1.5278     5.1258   0.298    0.767
## salinity:ph  -0.1099     0.2276  -0.483    0.632
## 
## Residual standard error: 0.9798 on 41 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.121,  Adjusted R-squared:  0.05672 
## F-statistic: 1.882 on 3 and 41 DF,  p-value: 0.1477
anova (lm1)
## Analysis of Variance Table
## 
## Response: covcl.repro
##             Df Sum Sq Mean Sq F value  Pr(>F)  
## salinity     1  4.623  4.6228  4.8157 0.03392 *
## ph           1  0.573  0.5734  0.5973 0.44405  
## salinity:ph  1  0.224  0.2236  0.2329 0.63191  
## Residuals   41 39.358  0.9600                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on cover class of reproductive tissue, interaction term removed

lm2 <- lm(covcl.repro ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = covcl.repro ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    10.29263     -0.04635     -0.88375
summary (lm2)
## 
## Call:
## lm(formula = covcl.repro ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5771 -0.8418  0.0562  0.6848  2.2130 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 10.29263    8.99114   1.145   0.2588  
## salinity    -0.04635    0.02043  -2.268   0.0285 *
## ph          -0.88375    1.13302  -0.780   0.4398  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9708 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.116,  Adjusted R-squared:  0.07395 
## F-statistic: 2.757 on 2 and 42 DF,  p-value: 0.075
anova (lm2)
## Analysis of Variance Table
## 
## Response: covcl.repro
##           Df Sum Sq Mean Sq F value  Pr(>F)  
## salinity   1  4.623  4.6228  4.9053 0.03225 *
## ph         1  0.573  0.5734  0.6084 0.43977  
## Residuals 42 39.582  0.9424                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on cover class of reproductive tissue: daily minimum salinity

lm3 <- lm(covcl.repro ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = covcl.repro ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##       9.24285       -0.04474       -0.76815
summary (lm3)
## 
## Call:
## lm(formula = covcl.repro ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5529 -0.7777  0.1053  0.5568  2.2357 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    9.24285    8.85908   1.043   0.3028  
## daily.min.sal -0.04474    0.01827  -2.449   0.0186 *
## ph            -0.76815    1.12006  -0.686   0.4966  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9621 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1318, Adjusted R-squared:  0.09041 
## F-statistic: 3.187 on 2 and 42 DF,  p-value: 0.05146
anova (lm3)
## Analysis of Variance Table
## 
## Response: covcl.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.sal  1  5.465  5.4645  5.9034 0.01947 *
## ph             1  0.435  0.4354  0.4703 0.49660  
## Residuals     42 38.878  0.9257                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on cover class of reproductive tissue: daily maximum salinity

lm4 <- lm(covcl.repro ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = covcl.repro ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      10.31818       -0.04071       -0.88930
summary (lm4)
## 
## Call:
## lm(formula = covcl.repro ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.54759 -0.97042 -0.03736  0.83889  2.36405 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   10.31818    9.33983   1.105    0.276
## daily.max.sal -0.04071    0.02601  -1.565    0.125
## ph            -0.88930    1.17067  -0.760    0.452
## 
## Residual standard error: 0.9998 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.06243,    Adjusted R-squared:  0.01779 
## F-statistic: 1.398 on 2 and 42 DF,  p-value: 0.2582
anova (lm4)
## Analysis of Variance Table
## 
## Response: covcl.repro
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.max.sal  1  2.219 2.21884  2.2198 0.1437
## ph             1  0.577 0.57682  0.5771 0.4517
## Residuals     42 41.982 0.99957
plot (lm4)

Effect and salinity and pH on cover class of reproductive tissue: daily salinity range

lm5 <- lm(covcl.repro ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##          4.8457           0.0717          -0.3771
summary (lm5)
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3920 -0.8882  0.0634  0.6154  2.6924 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       4.8457     9.0491   0.535    0.595  
## daily.sal.range   0.0717     0.0331   2.166    0.036 *
## ph               -0.3771     1.1440  -0.330    0.743  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9755 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1074, Adjusted R-squared:  0.06494 
## F-statistic: 2.528 on 2 and 42 DF,  p-value: 0.09191
anova (lm5)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1  4.708  4.7077  4.9472 0.03156 *
## ph               1  0.103  0.1034  0.1087 0.74332  
## Residuals       42 39.967  0.9516                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on cover class of reproductive tissue: daily median salinity

lm6 <- lm(covcl.repro ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = covcl.repro ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      10.20828       -0.04482       -0.87844
summary (lm6)
## 
## Call:
## lm(formula = covcl.repro ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.56463 -0.84769  0.04697  0.71379  2.21816 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   10.20828    9.02555   1.131   0.2645  
## daily.med.sal -0.04482    0.02049  -2.188   0.0343 *
## ph            -0.87844    1.13739  -0.772   0.4442  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9745 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1093, Adjusted R-squared:  0.06685 
## F-statistic: 2.576 on 2 and 42 DF,  p-value: 0.08804
anova (lm6)
## Analysis of Variance Table
## 
## Response: covcl.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.sal  1  4.326  4.3263  4.5558 0.03869 *
## ph             1  0.566  0.5665  0.5965 0.44425  
## Residuals     42 39.885  0.9496                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily minimun salinity less than 5

lm7 <- lm(covcl.repro ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##           5.11483           -0.01704           -0.34397
summary (lm7)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44926 -0.95458 -0.03976  0.69955  2.65393 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        5.11483    9.81278   0.521    0.605
## min.daily.sal.lt5 -0.01704    0.01990  -0.856    0.397
## ph                -0.34397    1.25170  -0.275    0.785
## 
## Residual standard error: 1.02 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02477,    Adjusted R-squared:  -0.02167 
## F-statistic: 0.5334 on 2 and 42 DF,  p-value: 0.5905
anova (lm7)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                   Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1  1.031 1.03063  0.9913 0.3251
## ph                 1  0.079 0.07851  0.0755 0.7848
## Residuals         42 43.669 1.03973
plot (lm7)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily minimun salinity less than 10

lm8 <- lm(covcl.repro ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##           11.19180             0.02091            -1.17050
summary (lm8)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4885 -0.8487 -0.1482  0.6663  2.6494 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        11.19180    9.84578   1.137    0.262
## min.daily.sal.lt10  0.02091    0.01883   1.111    0.273
## ph                 -1.17050    1.25792  -0.931    0.357
## 
## Residual standard error: 1.014 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03606,    Adjusted R-squared:  -0.009842 
## F-statistic: 0.7856 on 2 and 42 DF,  p-value: 0.4624
anova (lm8)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1  0.725 0.72489  0.7054 0.4057
## ph                  1  0.890 0.88981  0.8658 0.3574
## Residuals          42 43.163 1.02769
plot (lm8)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily minimun salinity less than 15

lm9 <- lm(covcl.repro ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##           10.40649             0.03342            -1.09753
summary (lm9)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.60501 -0.76945 -0.07025  0.66038  2.40448 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        10.40649    9.13338   1.139   0.2610  
## min.daily.sal.lt15  0.03342    0.01676   1.995   0.0526 .
## ph                 -1.09753    1.16241  -0.944   0.3505  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.983 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0936, Adjusted R-squared:  0.05043 
## F-statistic: 2.168 on 2 and 42 DF,  p-value: 0.127
anova (lm9)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt15  1  3.330  3.3296  3.4455 0.07045 .
## ph                  1  0.861  0.8615  0.8915 0.35048  
## Residuals          42 40.587  0.9664                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily maximum salinity less than 5

lm10 <- lm(covcl.repro ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          6.133029          -0.008221          -0.484522
summary (lm10)
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3940 -1.0842 -0.1244  0.7232  2.7514 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        6.133029  10.063524   0.609    0.546
## max.daily.sal.lt5 -0.008221   0.019392  -0.424    0.674
## ph                -0.484522   1.284763  -0.377    0.708
## 
## Residual standard error: 1.026 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01197,    Adjusted R-squared:  -0.03508 
## F-statistic: 0.2545 on 2 and 42 DF,  p-value: 0.7765
anova (lm10)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1  0.386 0.38634  0.3668  0.548
## ph                 1  0.150 0.14982  0.1422  0.708
## Residuals         42 44.242 1.05337
plot (lm10)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily maximum salinity less than 10

lm11 <- lm(covcl.repro ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##            5.80896            -0.00992            -0.44065
summary (lm11)
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4061 -1.0699 -0.1096  0.7151  2.7261 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)         5.80896   10.08546   0.576    0.568
## max.daily.sal.lt10 -0.00992    0.01969  -0.504    0.617
## ph                 -0.44065    1.28827  -0.342    0.734
## 
## Residual standard error: 1.025 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01371,    Adjusted R-squared:  -0.03326 
## F-statistic: 0.2918 on 2 and 42 DF,  p-value: 0.7484
anova (lm11)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1  0.491 0.49072  0.4667 0.4983
## ph                  1  0.123 0.12302  0.1170 0.7340
## Residuals          42 44.164 1.05152
plot (lm11)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily maximum salinity less than 15

lm12 <- lm(covcl.repro ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##           9.579685            0.009411           -0.947149
summary (lm12)
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3419 -1.0119 -0.1341  0.6860  2.7905 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         9.579685  10.213595   0.938    0.354
## max.daily.sal.lt15  0.009411   0.019079   0.493    0.624
## ph                 -0.947149   1.306128  -0.725    0.472
## 
## Residual standard error: 1.026 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01346,    Adjusted R-squared:  -0.03352 
## F-statistic: 0.2865 on 2 and 42 DF,  p-value: 0.7523
anova (lm12)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1  0.050 0.04966  0.0472 0.8290
## ph                  1  0.553 0.55309  0.5259 0.4724
## Residuals          42 44.175 1.05179
plot (lm12)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily salinity range greater than 10

lm13 <- lm(covcl.repro ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             6.828108             -0.004844             -0.577111
summary (lm13)
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3747 -1.1095 -0.1139  0.7363  2.7936 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)           6.828108   9.975005   0.685    0.497
## daily.sal.range.gt10 -0.004844   0.019528  -0.248    0.805
## ph                   -0.577111   1.273019  -0.453    0.653
## 
## Residual standard error: 1.028 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.009197,   Adjusted R-squared:  -0.03798 
## F-statistic: 0.1949 on 2 and 42 DF,  p-value: 0.8236
anova (lm13)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1  0.195 0.19473  0.1843 0.6699
## ph                    1  0.217 0.21709  0.2055 0.6526
## Residuals            42 44.366 1.05633
plot (lm13)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily salinity range greater than 5

lm14 <- lm(covcl.repro ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##             7.37454              0.02273             -0.69623
summary (lm14)
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9229 -0.8971 -0.1017  0.6520  2.6269 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          7.37454    9.26418   0.796    0.430
## daily.sal.range.gt5  0.02273    0.01759   1.292    0.203
## ph                  -0.69623    1.17376  -0.593    0.556
## 
## Residual standard error: 1.009 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.04569,    Adjusted R-squared:  0.0002491 
## F-statistic: 1.005 on 2 and 42 DF,  p-value: 0.3745
anova (lm14)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                     Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1  1.688 1.68803  1.6591 0.2048
## ph                   1  0.358 0.35797  0.3518 0.5563
## Residuals           42 42.732 1.01742
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on cover class of reproductive tissue: daily minimum ph

lm3 <- lm(covcl.repro ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = covcl.repro ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##      5.20736      -0.24391      -0.04567
summary (lm3)
## 
## Call:
## lm(formula = covcl.repro ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.45632 -0.91051  0.04227  0.67781  2.26284 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   5.20736   12.20817   0.427   0.6719  
## daily.min.ph -0.24391    1.55216  -0.157   0.8759  
## salinity     -0.04567    0.02081  -2.195   0.0338 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9775 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1038, Adjusted R-squared:  0.06109 
## F-statistic: 2.431 on 2 and 42 DF,  p-value: 0.1002
anova (lm3)
## Analysis of Variance Table
## 
## Response: covcl.repro
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.ph  1  0.044  0.0443  0.0463 0.83059  
## salinity      1  4.602  4.6021  4.8164 0.03377 *
## Residuals    42 40.131  0.9555                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on cover class of reproductive tissue: daily maximum ph

lm4 <- lm(covcl.repro ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = covcl.repro ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     10.43961      -0.88953      -0.04719
summary (lm4)
## 
## Call:
## lm(formula = covcl.repro ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.57396 -0.83045 -0.02016  0.70609  2.20917 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  10.43961    6.54944   1.594   0.1184  
## daily.max.ph -0.88953    0.81255  -1.095   0.2799  
## salinity     -0.04719    0.02032  -2.323   0.0251 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9641 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1281, Adjusted R-squared:  0.0866 
## F-statistic: 3.086 on 2 and 42 DF,  p-value: 0.05618
anova (lm4)
## Analysis of Variance Table
## 
## Response: covcl.repro
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.max.ph  1  0.723  0.7226  0.7773 0.38297  
## salinity      1  5.014  5.0143  5.3943 0.02512 *
## Residuals    42 39.041  0.9295                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on cover class of reproductive tissue: daily ph range

lm5 <- lm(covcl.repro ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = covcl.repro ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        3.29131         0.05334        -0.04629
summary (lm5)
## 
## Call:
## lm(formula = covcl.repro ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3924 -0.9106  0.0689  0.6824  2.3229 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     3.29131    0.50535   6.513 7.26e-08 ***
## daily.ph.range  0.05334    0.09460   0.564   0.5759    
## salinity       -0.04629    0.02055  -2.253   0.0295 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9741 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:   0.11,  Adjusted R-squared:  0.06759 
## F-statistic: 2.595 on 2 and 42 DF,  p-value: 0.08658
anova (lm5)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.ph.range  1  0.108  0.1078  0.1136 0.73771  
## salinity        1  4.817  4.8166  5.0761 0.02954 *
## Residuals      42 39.853  0.9489                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on cover class of reproductive tissue: daily median ph

lm6 <- lm(covcl.repro ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = covcl.repro ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##      9.99238      -0.84377      -0.04675
summary (lm6)
## 
## Call:
## lm(formula = covcl.repro ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.57664 -0.84448  0.05265  0.67386  2.19726 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   9.99238    8.70436   1.148   0.2575  
## daily.med.ph -0.84377    1.09406  -0.771   0.4449  
## salinity     -0.04675    0.02048  -2.283   0.0276 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9709 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1158, Adjusted R-squared:  0.07365 
## F-statistic: 2.749 on 2 and 42 DF,  p-value: 0.0755
anova (lm6)
## Analysis of Variance Table
## 
## Response: covcl.repro
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.ph  1  0.272  0.2721  0.2886 0.59395  
## salinity      1  4.911  4.9115  5.2099 0.02759 *
## Residuals    42 39.594  0.9427                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on cover class of reproductive tissue: number of days with a daily minimun ph less than 7

lm7 <- lm(covcl.repro ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          3.24221           0.01419          -0.04875
summary (lm7)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.29855 -0.86299 -0.07886  0.77366  2.40167 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       3.24221    0.50818   6.380 1.13e-07 ***
## min.daily.ph.lt7  0.01419    0.01915   0.741    0.463    
## salinity         -0.04875    0.02096  -2.325    0.025 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9715 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1148, Adjusted R-squared:  0.07267 
## F-statistic: 2.724 on 2 and 42 DF,  p-value: 0.0772
anova (lm7)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                  Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.ph.lt7  1  0.039  0.0392  0.0415 0.83947  
## salinity          1  5.102  5.1023  5.4066 0.02497 *
## Residuals        42 39.636  0.9437                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on cover class of reproductive tissue: number of days with a daily minimun ph less than 8

lm7 <- lm(covcl.repro ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##         3.344956         -0.002266         -0.044985
summary (lm7)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.36358 -0.90932  0.03209  0.72846  2.33484 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       3.344956   0.556423   6.012 3.82e-07 ***
## min.daily.ph.lt8 -0.002266   0.009563  -0.237   0.8139    
## salinity         -0.044985   0.020511  -2.193   0.0339 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9771 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1044, Adjusted R-squared:  0.06179 
## F-statistic: 2.449 on 2 and 42 DF,  p-value: 0.09863
anova (lm7)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                  Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.ph.lt8  1  0.084  0.0836  0.0876 0.76876  
## salinity          1  4.593  4.5928  4.8103 0.03388 *
## Residuals        42 40.101  0.9548                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on cover class of reproductive tissue: number of days with a daily maximum ph less than 7

lm10 <- lm(covcl.repro ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          3.24221           0.01419          -0.04875
summary (lm10)
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.29855 -0.86299 -0.07886  0.77366  2.40167 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       3.24221    0.50818   6.380 1.13e-07 ***
## max.daily.ph.lt7  0.01419    0.01915   0.741    0.463    
## salinity         -0.04875    0.02096  -2.325    0.025 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9715 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1148, Adjusted R-squared:  0.07267 
## F-statistic: 2.724 on 2 and 42 DF,  p-value: 0.0772
anova (lm10)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                  Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.ph.lt7  1  0.039  0.0392  0.0415 0.83947  
## salinity          1  5.102  5.1023  5.4066 0.02497 *
## Residuals        42 39.636  0.9437                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect salinity and pH on cover class of reproductive tissue: number of days with a daily ph range greater than 0.5

lm13 <- lm(covcl.repro ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = covcl.repro ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##             3.258127              0.008066             -0.047279
summary (lm13)
## 
## Call:
## lm(formula = covcl.repro ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3372 -0.8919 -0.0460  0.7696  2.3721 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.258127   0.511343   6.372 1.16e-07 ***
## daily.ph.range.gt0.5  0.008066   0.018186   0.444     0.66    
## salinity             -0.047279   0.021042  -2.247     0.03 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9755 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1074, Adjusted R-squared:  0.06492 
## F-statistic: 2.527 on 2 and 42 DF,  p-value: 0.09196
anova (lm13)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                      Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.ph.range.gt0.5  1  0.006  0.0060  0.0063 0.93699  
## salinity              1  4.804  4.8040  5.0483 0.02996 *
## Residuals            42 39.968  0.9516                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

####Q2.2 Effects of salinity and pH on dry weight of reproductive tissue#### Different salinity terms first

Effect of pH and salinity on dry weight of reproductive tissue

lm1 <- lm(dw.repro ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = dw.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##    31.13562     -0.26994     -3.84309      0.03593
summary (lm1)
## 
## Call:
## lm(formula = dw.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4688 -0.7341 -0.3593  0.6175  3.0439 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.13562   46.43263   0.671    0.507
## salinity    -0.26994    2.07795  -0.130    0.897
## ph          -3.84309    5.87522  -0.654    0.517
## salinity:ph  0.03593    0.26313   0.137    0.892
## 
## Residual standard error: 1.103 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1399, Adjusted R-squared:  0.07196 
## F-statistic:  2.06 on 3 and 38 DF,  p-value: 0.1218
anova (lm1)
## Analysis of Variance Table
## 
## Response: dw.repro
##             Df Sum Sq Mean Sq F value Pr(>F)  
## salinity     1  0.779  0.7793  0.6406 0.4285  
## ph           1  6.715  6.7149  5.5199 0.0241 *
## salinity:ph  1  0.023  0.0227  0.0186 0.8921  
## Residuals   38 46.226  1.2165                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on dry weight of reproductive tissue, interaction term removed

lm2 <- lm(dw.repro ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = dw.repro ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    24.95524      0.01375     -3.06088
summary (lm2)
## 
## Call:
## lm(formula = dw.repro ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4016 -0.7319 -0.3559  0.6077  3.1059 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 24.95524   10.21256   2.444   0.0192 *
## salinity     0.01375    0.02325   0.592   0.5576  
## ph          -3.06088    1.28631  -2.380   0.0223 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.089 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1394, Adjusted R-squared:  0.09531 
## F-statistic:  3.16 on 2 and 39 DF,  p-value: 0.05348
anova (lm2)
## Analysis of Variance Table
## 
## Response: dw.repro
##           Df Sum Sq Mean Sq F value  Pr(>F)  
## salinity   1  0.779  0.7793  0.6571 0.42249  
## ph         1  6.715  6.7149  5.6624 0.02232 *
## Residuals 39 46.249  1.1859                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on dry weight of reproductive tissue: daily minimum salinity

lm3 <- lm(dw.repro ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = dw.repro ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##     25.410152       0.009347      -3.102794
summary (lm3)
## 
## Call:
## lm(formula = dw.repro ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4554 -0.7654 -0.3559  0.6382  3.0584 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   25.410152  10.168768   2.499   0.0168 *
## daily.min.sal  0.009347   0.021021   0.445   0.6590  
## ph            -3.102794   1.284943  -2.415   0.0205 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.091 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1361, Adjusted R-squared:  0.0918 
## F-statistic: 3.072 on 2 and 39 DF,  p-value: 0.05768
anova (lm3)
## Analysis of Variance Table
## 
## Response: dw.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.sal  1  0.373  0.3730  0.3133 0.57885  
## ph             1  6.942  6.9416  5.8309 0.02054 *
## Residuals     39 46.429  1.1905                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on dry weight of reproductive tissue: daily maximum salinity

lm4 <- lm(dw.repro ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = dw.repro ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      24.22918        0.02332       -3.00580
summary (lm4)
## 
## Call:
## lm(formula = dw.repro ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3356 -0.7312 -0.3208  0.5439  3.1691 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   24.22918   10.25445   2.363   0.0232 *
## daily.max.sal  0.02332    0.02860   0.815   0.4198  
## ph            -3.00580    1.28496  -2.339   0.0245 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.085 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1463, Adjusted R-squared:  0.1025 
## F-statistic: 3.341 on 2 and 39 DF,  p-value: 0.04578
anova (lm4)
## Analysis of Variance Table
## 
## Response: dw.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.max.sal  1  1.424  1.4239  1.2103 0.27801  
## ph             1  6.438  6.4375  5.4720 0.02454 *
## Residuals     39 45.882  1.1765                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on dry weight of reproductive tissue: daily salinity range

lm5 <- lm(dw.repro ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##       25.504381         0.009469        -3.095616
summary (lm5)
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5806 -0.6953 -0.4310  0.5628  2.9431 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     25.504381  10.221208   2.495   0.0169 *
## daily.sal.range  0.009469   0.037438   0.253   0.8016  
## ph              -3.095616   1.292810  -2.394   0.0215 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.093 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1331, Adjusted R-squared:  0.08869 
## F-statistic: 2.995 on 2 and 39 DF,  p-value: 0.06165
anova (lm5)
## Analysis of Variance Table
## 
## Response: dw.repro
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1  0.307  0.3066  0.2567 0.61526  
## ph               1  6.849  6.8490  5.7336 0.02155 *
## Residuals       39 46.587  1.1945                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on dry weight of reproductive tissue: daily median salinity

lm6 <- lm(dw.repro ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = dw.repro ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      24.83787        0.01559       -3.05116
summary (lm6)
## 
## Call:
## lm(formula = dw.repro ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3838 -0.7273 -0.3620  0.6045  3.1260 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   24.83787   10.19975   2.435   0.0196 *
## daily.med.sal  0.01559    0.02321   0.672   0.5058  
## ph            -3.05116    1.28473  -2.375   0.0226 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.088 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1416, Adjusted R-squared:  0.09763 
## F-statistic: 3.218 on 2 and 39 DF,  p-value: 0.05087
anova (lm6)
## Analysis of Variance Table
## 
## Response: dw.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.sal  1  0.941  0.9411  0.7956 0.37788  
## ph             1  6.672  6.6716  5.6404 0.02257 *
## Residuals     39 46.130  1.1828                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun salinity less than 5

lm7 <- lm(dw.repro ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          22.61891           -0.02302           -2.69329
summary (lm7)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6741 -0.6484 -0.3834  0.4912  2.8205 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       22.61891   10.45603   2.163   0.0367 *
## min.daily.sal.lt5 -0.02302    0.02173  -1.059   0.2960  
## ph                -2.69329    1.33391  -2.019   0.0504 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.078 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.156,  Adjusted R-squared:  0.1127 
## F-statistic: 3.604 on 2 and 39 DF,  p-value: 0.03661
anova (lm7)
## Analysis of Variance Table
## 
## Response: dw.repro
##                   Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt5  1  3.643  3.6426  3.1319 0.08459 .
## ph                 1  4.741  4.7415  4.0767 0.05039 .
## Residuals         39 45.359  1.1631                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun salinity less than 10

lm8 <- lm(dw.repro ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##           22.87712            -0.01797            -2.72730
summary (lm8)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5367 -0.6482 -0.3788  0.5694  2.9988 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        22.87712   10.61648   2.155   0.0374 *
## min.daily.sal.lt10 -0.01797    0.02077  -0.865   0.3922  
## ph                 -2.72730    1.35685  -2.010   0.0514 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.084 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1481, Adjusted R-squared:  0.1044 
## F-statistic: 3.389 on 2 and 39 DF,  p-value: 0.04394
anova (lm8)
## Analysis of Variance Table
## 
## Response: dw.repro
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt10  1  3.215  3.2149  2.7385 0.10598  
## ph                  1  4.743  4.7431  4.0402 0.05138 .
## Residuals          39 45.785  1.1740                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm8)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun salinity less than 15

lm9 <- lm(dw.repro ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##          25.178166           -0.008509           -3.032667
summary (lm9)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4790 -0.8003 -0.4077  0.5870  3.1237 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        25.178166  10.230774   2.461   0.0184 *
## min.daily.sal.lt15 -0.008509   0.019234  -0.442   0.6606  
## ph                 -3.032667   1.302205  -2.329   0.0251 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.091 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1361, Adjusted R-squared:  0.09175 
## F-statistic: 3.071 on 2 and 39 DF,  p-value: 0.05774
anova (lm9)
## Analysis of Variance Table
## 
## Response: dw.repro
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt15  1  0.855  0.8552  0.7183 0.40186  
## ph                  1  6.457  6.4570  5.4236 0.02514 *
## Residuals          39 46.431  1.1905                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily maximum salinity less than 5

lm10 <- lm(dw.repro ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          23.53880           -0.01309           -2.82273
summary (lm10)
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6302 -0.7704 -0.3788  0.5414  2.8873 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       23.53880   10.75667   2.188   0.0347 *
## max.daily.sal.lt5 -0.01309    0.02117  -0.618   0.5400  
## ph                -2.82273    1.37351  -2.055   0.0466 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.089 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1401, Adjusted R-squared:  0.09605 
## F-statistic: 3.178 on 2 and 39 DF,  p-value: 0.05263
anova (lm10)
## Analysis of Variance Table
## 
## Response: dw.repro
##                   Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt5  1  2.528  2.5276  2.1331 0.15216  
## ph                 1  5.004  5.0045  4.2236 0.04661 *
## Residuals         39 46.211  1.1849                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily maximum salinity less than 10

lm11 <- lm(dw.repro ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           22.23598            -0.02035            -2.64664
summary (lm11)
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6761 -0.6868 -0.3264  0.5458  2.8415 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        22.23598   10.72178   2.074   0.0447 *
## max.daily.sal.lt10 -0.02035    0.02140  -0.951   0.3476  
## ph                 -2.64664    1.36987  -1.932   0.0606 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.081 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1514, Adjusted R-squared:  0.1079 
## F-statistic: 3.479 on 2 and 39 DF,  p-value: 0.04072
anova (lm11)
## Analysis of Variance Table
## 
## Response: dw.repro
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt10  1  3.771  3.7712  3.2249 0.08028 .
## ph                  1  4.365  4.3651  3.7327 0.06064 .
## Residuals          39 45.607  1.1694                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm11)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily maximum salinity less than 15

lm12 <- lm(dw.repro ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##           20.69581            -0.02547            -2.44003
summary (lm12)
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6453 -0.6547 -0.3332  0.5561  2.7959 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        20.69581   10.78403   1.919   0.0623 .
## max.daily.sal.lt15 -0.02547    0.02057  -1.239   0.2229  
## ph                 -2.44003    1.37960  -1.769   0.0848 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.073 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1646, Adjusted R-squared:  0.1217 
## F-statistic: 3.842 on 2 and 39 DF,  p-value: 0.03
anova (lm12)
## Analysis of Variance Table
## 
## Response: dw.repro
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt15  1  5.244  5.2440  4.5551 0.03916 *
## ph                  1  3.601  3.6012  3.1281 0.08477 .
## Residuals          39 44.898  1.1512                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm12)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily salinity range greater than 10

lm13 <- lm(dw.repro ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             24.00909              -0.01151              -2.88429
summary (lm13)
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5821 -0.7682 -0.3934  0.5444  2.9239 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          24.00909   10.65862   2.253   0.0300 *
## daily.sal.range.gt10 -0.01151    0.02134  -0.539   0.5928  
## ph                   -2.88429    1.36049  -2.120   0.0404 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.09 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1381, Adjusted R-squared:  0.09395 
## F-statistic: 3.126 on 2 and 39 DF,  p-value: 0.05507
anova (lm13)
## Analysis of Variance Table
## 
## Response: dw.repro
##                      Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt10  1  2.087  2.0865  1.7568 0.19273  
## ph                    1  5.338  5.3380  4.4946 0.04042 *
## Residuals            39 46.319  1.1877                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily salinity range greater than 5

lm14 <- lm(dw.repro ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##           25.912448            -0.004355            -3.132990
summary (lm14)
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5453 -0.7432 -0.3963  0.6046  3.0246 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         25.912448  10.153682   2.552   0.0147 *
## daily.sal.range.gt5 -0.004355   0.019442  -0.224   0.8239  
## ph                  -3.132990   1.285995  -2.436   0.0195 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.093 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1328, Adjusted R-squared:  0.08837 
## F-statistic: 2.987 on 2 and 39 DF,  p-value: 0.06208
anova (lm14)
## Analysis of Variance Table
## 
## Response: dw.repro
##                     Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt5  1  0.047  0.0467  0.0391 0.84430  
## ph                   1  7.092  7.0925  5.9353 0.01951 *
## Residuals           39 46.604  1.1950                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on dry weight of reproductive tissue: daily minimum ph

lm3 <- lm(dw.repro ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = dw.repro ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##    29.688242     -3.687621      0.009348
summary (lm3)
## 
## Call:
## lm(formula = dw.repro ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3003 -0.7530 -0.3519  0.5790  2.8895 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  29.688242  14.050000   2.113   0.0410 *
## daily.min.ph -3.687621   1.785267  -2.066   0.0456 *
## salinity      0.009348   0.023959   0.390   0.6985  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.106 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1117, Adjusted R-squared:  0.06613 
## F-statistic: 2.452 on 2 and 39 DF,  p-value: 0.09933
anova (lm3)
## Analysis of Variance Table
## 
## Response: dw.repro
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.ph  1  5.816  5.8158  4.7510 0.03538 *
## salinity      1  0.186  0.1864  0.1522 0.69853  
## Residuals    39 47.741  1.2241                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on dry weight of reproductive tissue: daily maximum ph

lm4 <- lm(dw.repro ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = dw.repro ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     10.14322      -1.17620       0.01599
summary (lm4)
## 
## Call:
## lm(formula = dw.repro ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1938 -0.7965 -0.3078  0.5169  3.4933 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  10.14322    7.96870   1.273    0.211
## daily.max.ph -1.17620    0.98884  -1.189    0.241
## salinity      0.01599    0.02445   0.654    0.517
## 
## Residual standard error: 1.145 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.049,  Adjusted R-squared:  0.0002317 
## F-statistic: 1.005 on 2 and 39 DF,  p-value: 0.3754
anova (lm4)
## Analysis of Variance Table
## 
## Response: dw.repro
##              Df Sum Sq Mean Sq F value Pr(>F)
## daily.max.ph  1  2.073  2.0729  1.5817 0.2160
## salinity      1  0.561  0.5606  0.4278 0.5169
## Residuals    39 51.110  1.3105
plot (lm4)

Effect of salinity and pH on dry weight of reproductive tissue: daily ph range

lm5 <- lm(dw.repro ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = dw.repro ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        0.68265         0.06812         0.01812
summary (lm5)
## 
## Call:
## lm(formula = dw.repro ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1043 -0.7328 -0.4055  0.3517  3.7371 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.68265    0.60691   1.125    0.268
## daily.ph.range  0.06812    0.16005   0.426    0.673
## salinity        0.01812    0.02476   0.732    0.469
## 
## Residual standard error: 1.163 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.01906,    Adjusted R-squared:  -0.03125 
## F-statistic: 0.3788 on 2 and 39 DF,  p-value: 0.6872
anova (lm5)
## Analysis of Variance Table
## 
## Response: dw.repro
##                Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range  1  0.300 0.30048  0.2223 0.6399
## salinity        1  0.724 0.72366  0.5353 0.4687
## Residuals      39 52.719 1.35177
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on dry weight of reproductive tissue: daily median ph

lm6 <- lm(dw.repro ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = dw.repro ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     17.79898      -2.15310       0.01429
summary (lm6)
## 
## Call:
## lm(formula = dw.repro ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2422 -0.7931 -0.3330  0.5802  3.2650 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  17.79898   10.25818   1.735   0.0906 .
## daily.med.ph -2.15310    1.28893  -1.670   0.1028  
## salinity      0.01429    0.02408   0.593   0.5565  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.126 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0803, Adjusted R-squared:  0.03314 
## F-statistic: 1.703 on 2 and 39 DF,  p-value: 0.1955
anova (lm6)
## Analysis of Variance Table
## 
## Response: dw.repro
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.ph  1  3.870  3.8698  3.0534 0.08844 .
## salinity      1  0.446  0.4460  0.3519 0.55648  
## Residuals    39 49.427  1.2674                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun ph less than 7

lm7 <- lm(dw.repro ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = dw.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          0.83663          -0.03840           0.02817
summary (lm7)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3545 -0.7924 -0.3262  0.4901  3.4995 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.83663    0.59272   1.412   0.1660  
## min.daily.ph.lt7 -0.03840    0.02254  -1.704   0.0964 .
## salinity          0.02817    0.02453   1.148   0.2579  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.124 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08275,    Adjusted R-squared:  0.03572 
## F-statistic: 1.759 on 2 and 39 DF,  p-value: 0.1856
anova (lm7)
## Analysis of Variance Table
## 
## Response: dw.repro
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1  2.781  2.7809  2.2001 0.1460
## salinity          1  1.667  1.6665  1.3185 0.2579
## Residuals        39 49.296  1.2640
plot (lm7)

Effect salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun ph less than 8

lm7 <- lm(dw.repro ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = dw.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##          0.83957          -0.00619           0.01909
summary (lm7)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0914 -0.7498 -0.4160  0.4656  3.7789 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.83957    0.66700   1.259    0.216
## min.daily.ph.lt8 -0.00619    0.01165  -0.531    0.598
## salinity          0.01909    0.02469   0.773    0.444
## 
## Residual standard error: 1.161 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.02158,    Adjusted R-squared:  -0.02859 
## F-statistic: 0.4301 on 2 and 39 DF,  p-value: 0.6535
anova (lm7)
## Analysis of Variance Table
## 
## Response: dw.repro
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt8  1  0.354 0.35431  0.2628 0.6111
## salinity          1  0.806 0.80556  0.5975 0.4442
## Residuals        39 52.583 1.34829
plot (lm7)

Effect salinity and pH on dry weight of reproductive tissue: number of days with a daily maximum ph less than 7

lm10 <- lm(dw.repro ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = dw.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          0.83663          -0.03840           0.02817
summary (lm10)
## 
## Call:
## lm(formula = dw.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3545 -0.7924 -0.3262  0.4901  3.4995 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.83663    0.59272   1.412   0.1660  
## max.daily.ph.lt7 -0.03840    0.02254  -1.704   0.0964 .
## salinity          0.02817    0.02453   1.148   0.2579  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.124 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08275,    Adjusted R-squared:  0.03572 
## F-statistic: 1.759 on 2 and 39 DF,  p-value: 0.1856
anova (lm10)
## Analysis of Variance Table
## 
## Response: dw.repro
##                  Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1  2.781  2.7809  2.2001 0.1460
## salinity          1  1.667  1.6665  1.3185 0.2579
## Residuals        39 49.296  1.2640
plot (lm10)

Effect salinity and pH on dry weight of reproductive tissue: number of days with a daily ph range greater than 0.5

lm13 <- lm(dw.repro ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = dw.repro ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              0.86255              -0.03930               0.02906
summary (lm13)
## 
## Call:
## lm(formula = dw.repro ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4004 -0.8123 -0.3435  0.4647  3.4624 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           0.86255    0.59010   1.462   0.1518  
## daily.ph.range.gt0.5 -0.03930    0.02119  -1.854   0.0713 .
## salinity              0.02906    0.02439   1.192   0.2406  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.117 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09434,    Adjusted R-squared:  0.0479 
## F-statistic: 2.031 on 2 and 39 DF,  p-value: 0.1448
anova (lm13)
## Analysis of Variance Table
## 
## Response: dw.repro
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1  3.298  3.2984  2.6429 0.1121
## salinity              1  1.772  1.7719  1.4198 0.2406
## Residuals            39 48.673  1.2480
plot (lm13)

####Q2.3 Effects of salinity and pH on number of reproductive apices#### Different salinity terms first

Effect of pH and salinity on number of reproductive apices

lm1 <- lm(apices.repro ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = apices.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##     536.774       -9.594      -64.948        1.236
summary (lm1)
## 
## Call:
## lm(formula = apices.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.428 -18.876  -7.324  13.701  63.708 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  536.774   1068.320   0.502    0.618
## salinity      -9.594     47.809  -0.201    0.842
## ph           -64.948    135.177  -0.480    0.634
## salinity:ph    1.236      6.054   0.204    0.839
## 
## Residual standard error: 25.38 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04587,    Adjusted R-squared:  -0.02945 
## F-statistic: 0.609 on 3 and 38 DF,  p-value: 0.6133
anova (lm1)
## Analysis of Variance Table
## 
## Response: apices.repro
##             Df  Sum Sq Mean Sq F value Pr(>F)
## salinity     1   112.2  112.15  0.1742 0.6788
## ph           1  1037.5 1037.52  1.6111 0.2121
## salinity:ph  1    26.8   26.82  0.0416 0.8394
## Residuals   38 24470.6  643.96
plot (lm1)

Effect of pH and salinity on number of reproductive apices, interaction term removed

lm2 <- lm(apices.repro ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = apices.repro ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    324.2267       0.1628     -38.0475
summary (lm2)
## 
## Call:
## lm(formula = apices.repro ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.260 -19.012  -8.198  13.238  65.841 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 324.2267   235.0413   1.379    0.176
## salinity      0.1628     0.5350   0.304    0.763
## ph          -38.0475    29.6044  -1.285    0.206
## 
## Residual standard error: 25.06 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04483,    Adjusted R-squared:  -0.004157 
## F-statistic: 0.9151 on 2 and 39 DF,  p-value: 0.4089
anova (lm2)
## Analysis of Variance Table
## 
## Response: apices.repro
##           Df  Sum Sq Mean Sq F value Pr(>F)
## salinity   1   112.2  112.15  0.1786 0.6749
## ph         1  1037.5 1037.52  1.6517 0.2063
## Residuals 39 24497.5  628.14
plot (lm2)

Effect and salinity and pH on number of reproductive apices: daily minimum salinity

lm3 <- lm(apices.repro ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = apices.repro ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      328.7130         0.1311       -38.4845
summary (lm3)
## 
## Call:
## lm(formula = apices.repro ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.453 -19.240  -8.257  13.665  65.502 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   328.7130   233.6370   1.407    0.167
## daily.min.sal   0.1311     0.4830   0.271    0.788
## ph            -38.4845    29.5228  -1.304    0.200
## 
## Residual standard error: 25.07 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04436,    Adjusted R-squared:  -0.004643 
## F-statistic: 0.9053 on 2 and 39 DF,  p-value: 0.4128
anova (lm3)
## Analysis of Variance Table
## 
## Response: apices.repro
##               Df  Sum Sq Mean Sq F value Pr(>F)
## daily.min.sal  1    69.9   69.92  0.1113 0.7405
## ph             1  1067.9 1067.88  1.6992 0.2000
## Residuals     39 24509.3  628.44
plot (lm3)

Effect and salinity and pH on number of reproductive apices: daily maximum salinity

lm4 <- lm(apices.repro ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = apices.repro ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##       317.478          0.249        -37.539
summary (lm4)
## 
## Call:
## lm(formula = apices.repro ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.872 -19.571  -8.567  12.729  66.342 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   317.4783   236.7979   1.341    0.188
## daily.max.sal   0.2490     0.6604   0.377    0.708
## ph            -37.5393    29.6725  -1.265    0.213
## 
## Residual standard error: 25.05 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04604,    Adjusted R-squared:  -0.002884 
## F-statistic: 0.941 on 2 and 39 DF,  p-value: 0.3989
anova (lm4)
## Analysis of Variance Table
## 
## Response: apices.repro
##               Df  Sum Sq Mean Sq F value Pr(>F)
## daily.max.sal  1   176.6  176.64  0.2816 0.5987
## ph             1  1004.1 1004.08  1.6005 0.2133
## Residuals     39 24466.4  627.34
plot (lm4)

Effect and salinity and pH on number of reproductive apices: daily salinity range

lm5 <- lm(apices.repro ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##        333.4372           0.0313         -38.7511
summary (lm5)
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.073 -20.422  -8.508  13.683  64.025 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)     333.4372   234.6582   1.421    0.163
## daily.sal.range   0.0313     0.8595   0.036    0.971
## ph              -38.7511    29.6803  -1.306    0.199
## 
## Residual standard error: 25.09 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04259,    Adjusted R-squared:  -0.006506 
## F-statistic: 0.8675 on 2 and 39 DF,  p-value: 0.428
anova (lm5)
## Analysis of Variance Table
## 
## Response: apices.repro
##                 Df  Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range  1    19.1   19.10  0.0303 0.8626
## ph               1  1073.3 1073.25  1.7046 0.1993
## Residuals       39 24554.8  629.61
plot (lm5)

Effect and salinity and pH on number of reproductive apices: daily median salinity

lm6 <- lm(apices.repro ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = apices.repro ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      321.9690         0.1983       -37.8630
summary (lm6)
## 
## Call:
## lm(formula = apices.repro ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.116 -19.297  -8.157  13.240  66.229 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   321.9690   234.9126   1.371    0.178
## daily.med.sal   0.1983     0.5346   0.371    0.713
## ph            -37.8630    29.5889  -1.280    0.208
## 
## Residual standard error: 25.05 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04593,    Adjusted R-squared:  -0.003002 
## F-statistic: 0.9386 on 2 and 39 DF,  p-value: 0.3998
anova (lm6)
## Analysis of Variance Table
## 
## Response: apices.repro
##               Df  Sum Sq Mean Sq F value Pr(>F)
## daily.med.sal  1   150.5  150.47  0.2398 0.6271
## ph             1  1027.4 1027.38  1.6375 0.2082
## Residuals     39 24469.3  627.42
plot (lm6)

Effect of salinity and pH on number of reproductive apices: number of days with a daily minimun salinity less than 5

lm7 <- lm(apices.repro ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          264.1771            -0.5053           -29.2806
summary (lm7)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -30.01 -16.65  -9.00  11.95  61.09 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)       264.1771   240.1473   1.100    0.278
## min.daily.sal.lt5  -0.5053     0.4991  -1.012    0.318
## ph                -29.2806    30.6364  -0.956    0.345
## 
## Residual standard error: 24.77 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06707,    Adjusted R-squared:  0.01923 
## F-statistic: 1.402 on 2 and 39 DF,  p-value: 0.2582
anova (lm7)
## Analysis of Variance Table
## 
## Response: apices.repro
##                   Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1  1159.9 1159.86  1.8905 0.1770
## ph                 1   560.4  560.41  0.9134 0.3451
## Residuals         39 23926.9  613.51
plot (lm7)

Effect of salinity and pH on number of reproductive apices: number of days with a daily minimun salinity less than 10

lm8 <- lm(apices.repro ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##           296.5024             -0.2318            -33.6715
summary (lm8)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.337 -18.934  -9.401  13.331  64.618 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        296.5024   245.1303   1.210    0.234
## min.daily.sal.lt10  -0.2318     0.4796  -0.483    0.632
## ph                 -33.6715    31.3292  -1.075    0.289
## 
## Residual standard error: 25.02 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04826,    Adjusted R-squared:  -0.000547 
## F-statistic: 0.9888 on 2 and 39 DF,  p-value: 0.3812
anova (lm8)
## Analysis of Variance Table
## 
## Response: apices.repro
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1   514.8  514.77  0.8225 0.3700
## ph                  1   723.0  722.97  1.1551 0.2891
## Residuals          39 24409.4  625.88
plot (lm8)

Effect of salinity and pH on number of reproductive apices: number of days with a daily minimun salinity less than 15

lm9 <- lm(apices.repro ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##         334.281419           -0.002712          -38.833342
summary (lm9)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.013 -20.390  -8.636  13.788  64.122 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)        334.281419 235.277433   1.421    0.163
## min.daily.sal.lt15  -0.002712   0.442323  -0.006    0.995
## ph                 -38.833342  29.946848  -1.297    0.202
## 
## Residual standard error: 25.09 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04256,    Adjusted R-squared:  -0.006539 
## F-statistic: 0.8668 on 2 and 39 DF,  p-value: 0.4282
anova (lm9)
## Analysis of Variance Table
## 
## Response: apices.repro
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1    32.8   32.80  0.0521 0.8207
## ph                  1  1058.7 1058.75  1.6815 0.2023
## Residuals          39 24555.6  629.63
plot (lm9)

Effect of salinity and pH on number of reproductive apices: number of days with a daily maximum salinity less than 5

lm10 <- lm(apices.repro ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##            280.42              -0.31             -31.59
summary (lm10)
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -28.66 -19.17  -8.49  13.30  62.44 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)       280.4213   246.6738   1.137    0.263
## max.daily.sal.lt5  -0.3100     0.4856  -0.638    0.527
## ph                -31.5908    31.4975  -1.003    0.322
## 
## Residual standard error: 24.96 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05246,    Adjusted R-squared:  0.003868 
## F-statistic:  1.08 on 2 and 39 DF,  p-value: 0.3497
anova (lm10)
## Analysis of Variance Table
## 
## Response: apices.repro
##                   Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1   718.6  718.63  1.1533 0.2895
## ph                 1   626.8  626.82  1.0059 0.3221
## Residuals         39 24301.7  623.12
plot (lm10)

Effect of salinity and pH on number of reproductive apices: number of days with a daily maximum salinity less than 10

lm11 <- lm(apices.repro ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           251.4388             -0.4713            -27.6726
summary (lm11)
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.688 -17.505  -7.964  12.006  61.412 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        251.4388   245.8979   1.023    0.313
## max.daily.sal.lt10  -0.4713     0.4909  -0.960    0.343
## ph                 -27.6726    31.4173  -0.881    0.384
## 
## Residual standard error: 24.8 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06467,    Adjusted R-squared:  0.0167 
## F-statistic: 1.348 on 2 and 39 DF,  p-value: 0.2716
anova (lm11)
## Analysis of Variance Table
## 
## Response: apices.repro
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1  1181.3 1181.29  1.9205 0.1737
## ph                  1   477.2  477.21  0.7758 0.3838
## Residuals          39 23988.6  615.09
plot (lm11)

Effect of salinity and pH on number of reproductive apices: number of days with a daily maximum salinity less than 15

lm12 <- lm(apices.repro ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##           247.2872             -0.4333            -27.1293
summary (lm12)
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.758 -16.622  -8.571  11.657  61.342 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        247.2872   249.5611   0.991    0.328
## max.daily.sal.lt15  -0.4333     0.4759  -0.910    0.368
## ph                 -27.1293    31.9263  -0.850    0.401
## 
## Residual standard error: 24.83 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06248,    Adjusted R-squared:  0.01441 
## F-statistic:   1.3 on 2 and 39 DF,  p-value: 0.2842
anova (lm12)
## Analysis of Variance Table
## 
## Response: apices.repro
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1  1157.4 1157.37  1.8772 0.1785
## ph                  1   445.2  445.18  0.7221 0.4007
## Residuals          39 24044.6  616.53
plot (lm12)

Effect of salinity and pH on number of reproductive apices: number of days with a daily salinity range greater than 10

lm13 <- lm(apices.repro ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             293.1616               -0.2623              -33.2656
summary (lm13)
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.292 -19.143  -8.844  13.604  63.333 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)          293.1616   244.5153   1.199    0.238
## daily.sal.range.gt10  -0.2623     0.4895  -0.536    0.595
## ph                   -33.2656    31.2104  -1.066    0.293
## 
## Residual standard error: 25 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04956,    Adjusted R-squared:  0.0008152 
## F-statistic: 1.017 on 2 and 39 DF,  p-value: 0.3712
anova (lm13)
## Analysis of Variance Table
## 
## Response: apices.repro
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1   560.9  560.91  0.8974 0.3493
## ph                    1   710.1  710.06  1.1360 0.2930
## Residuals            39 24376.2  625.03
plot (lm13)

Effect of salinity and pH on number of reproductive apices: number of days with a daily salinity range greater than 5

lm14 <- lm(apices.repro ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##           333.06990              0.06812            -38.81570
summary (lm14)
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.714 -20.005  -8.241  13.656  62.999 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)         333.06990  233.00108   1.429    0.161
## daily.sal.range.gt5   0.06812    0.44614   0.153    0.879
## ph                  -38.81570   29.51030  -1.315    0.196
## 
## Residual standard error: 25.08 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04313,    Adjusted R-squared:  -0.005939 
## F-statistic: 0.879 on 2 and 39 DF,  p-value: 0.4233
anova (lm14)
## Analysis of Variance Table
## 
## Response: apices.repro
##                     Df  Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1    17.5   17.52  0.0279 0.8683
## ph                   1  1088.7 1088.66  1.7301 0.1961
## Residuals           39 24540.9  629.25
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on number of reproductive apices: daily minimum ph

lm3 <- lm(apices.repro ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = apices.repro ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     387.4792      -46.4001        0.1066
summary (lm3)
## 
## Call:
## lm(formula = apices.repro ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.734 -17.628  -7.628  14.891  63.022 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  387.4792   319.6321   1.212    0.233
## daily.min.ph -46.4001    40.6141  -1.142    0.260
## salinity       0.1066     0.5451   0.196    0.846
## 
## Residual standard error: 25.17 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03661,    Adjusted R-squared:  -0.01279 
## F-statistic: 0.7411 on 2 and 39 DF,  p-value: 0.4832
anova (lm3)
## Analysis of Variance Table
## 
## Response: apices.repro
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.min.ph  1   914.8  914.82  1.4440 0.2367
## salinity      1    24.2   24.25  0.0383 0.8459
## Residuals    39 24708.1  633.54
plot (lm3)

Effect salinity and pH on number of reproductive apices: daily maximum ph

lm4 <- lm(apices.repro ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = apices.repro ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##       80.973        -7.262         0.208
summary (lm4)
## 
## Call:
## lm(formula = apices.repro ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -25.84 -18.73  -8.16  11.98  72.16 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)   80.9734   177.8696   0.455    0.651
## daily.max.ph  -7.2615    22.0719  -0.329    0.744
## salinity       0.2080     0.5457   0.381    0.705
## 
## Residual standard error: 25.55 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.007129,   Adjusted R-squared:  -0.04379 
## F-statistic:  0.14 on 2 and 39 DF,  p-value: 0.8698
anova (lm4)
## Analysis of Variance Table
## 
## Response: apices.repro
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.max.ph  1    88.0   87.97  0.1347 0.7156
## salinity      1    94.9   94.85  0.1453 0.7052
## Residuals    39 25464.3  652.93
plot (lm4)

Effect of salinity and pH on number of reproductive apices: daily ph range

lm5 <- lm(apices.repro ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = apices.repro ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        22.5527          0.5318          0.2201
summary (lm5)
## 
## Call:
## lm(formula = apices.repro ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.654 -17.883  -8.132   8.569  73.670 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     22.5527    13.3531   1.689   0.0992 .
## daily.ph.range   0.5318     3.5214   0.151   0.8807  
## salinity         0.2201     0.5448   0.404   0.6885  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.004955,   Adjusted R-squared:  -0.04607 
## F-statistic: 0.0971 on 2 and 39 DF,  p-value: 0.9077
anova (lm5)
## Analysis of Variance Table
## 
## Response: apices.repro
##                Df  Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range  1    20.3   20.31  0.0310 0.8611
## salinity        1   106.8  106.77  0.1632 0.6885
## Residuals      39 25520.0  654.36
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on number of reproductive apices: daily median ph

lm6 <- lm(apices.repro ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = apices.repro ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     185.4122      -20.4882        0.1825
summary (lm6)
## 
## Call:
## lm(formula = apices.repro ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.483 -18.962  -7.315  12.891  69.184 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  185.4122   231.6936   0.800    0.428
## daily.med.ph -20.4882    29.1120  -0.704    0.486
## salinity       0.1825     0.5439   0.336    0.739
## 
## Residual standard error: 25.43 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.01686,    Adjusted R-squared:  -0.03356 
## F-statistic: 0.3344 on 2 and 39 DF,  p-value: 0.7178
anova (lm6)
## Analysis of Variance Table
## 
## Response: apices.repro
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.med.ph  1   359.6  359.60  0.5562 0.4603
## salinity      1    72.8   72.78  0.1126 0.7390
## Residuals    39 25214.7  646.53
plot (lm6)

Effect salinity and pH on number of reproductive apices: number of days with a daily minimun ph less than 7

lm7 <- lm(apices.repro ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = apices.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          25.2914           -0.7057            0.3979
summary (lm7)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.056 -20.233  -4.932  11.647  69.343 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       25.2914    13.1587   1.922   0.0619 .
## min.daily.ph.lt7  -0.7057     0.5004  -1.410   0.1664  
## salinity           0.3979     0.5446   0.731   0.4694  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24.96 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05268,    Adjusted R-squared:  0.004102 
## F-statistic: 1.084 on 2 and 39 DF,  p-value: 0.3481
anova (lm7)
## Analysis of Variance Table
## 
## Response: apices.repro
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1  1018.6 1018.59  1.6350 0.2086
## salinity          1   332.6  332.56  0.5338 0.4694
## Residuals        39 24296.0  622.97
plot (lm7)

Effect salinity and pH on number of reproductive apices: number of days with a daily minimun ph less than 8

lm7 <- lm(apices.repro ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = apices.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##         21.65043           0.04047           0.22305
summary (lm7)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.678 -18.239  -8.551   8.949  73.343 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)      21.65043   14.69376   1.473    0.149
## min.daily.ph.lt8  0.04047    0.25667   0.158    0.876
## salinity          0.22305    0.54396   0.410    0.684
## 
## Residual standard error: 25.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.005007,   Adjusted R-squared:  -0.04602 
## F-statistic: 0.09813 on 2 and 39 DF,  p-value: 0.9067
anova (lm7)
## Analysis of Variance Table
## 
## Response: apices.repro
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt8  1    18.4   18.41  0.0281 0.8677
## salinity          1   110.0  110.02  0.1681 0.6840
## Residuals        39 25518.7  654.33
plot (lm7)

Effect salinity and pH on number of reproductive apices: number of days with a daily maximum ph less than 7

lm10 <- lm(apices.repro ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = apices.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          25.2914           -0.7057            0.3979
summary (lm10)
## 
## Call:
## lm(formula = apices.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.056 -20.233  -4.932  11.647  69.343 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       25.2914    13.1587   1.922   0.0619 .
## max.daily.ph.lt7  -0.7057     0.5004  -1.410   0.1664  
## salinity           0.3979     0.5446   0.731   0.4694  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24.96 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05268,    Adjusted R-squared:  0.004102 
## F-statistic: 1.084 on 2 and 39 DF,  p-value: 0.3481
anova (lm10)
## Analysis of Variance Table
## 
## Response: apices.repro
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1  1018.6 1018.59  1.6350 0.2086
## salinity          1   332.6  332.56  0.5338 0.4694
## Residuals        39 24296.0  622.97
plot (lm10)

Effect salinity and pH on number of reproductive apices: number of days with a daily ph range greater than 0.5

lm13 <- lm(apices.repro ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = apices.repro ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              25.5786               -0.6788                0.4030
summary (lm13)
## 
## Call:
## lm(formula = apices.repro ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.489 -20.325  -5.731  13.417  68.960 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           25.5786    13.1730   1.942   0.0594 .
## daily.ph.range.gt0.5  -0.6788     0.4731  -1.435   0.1593  
## salinity               0.4030     0.5445   0.740   0.4637  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24.94 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05429,    Adjusted R-squared:  0.005794 
## F-statistic: 1.119 on 2 and 39 DF,  p-value: 0.3367
anova (lm13)
## Analysis of Variance Table
## 
## Response: apices.repro
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1  1051.8 1051.75  1.6911 0.2011
## salinity              1   340.7  340.68  0.5478 0.4637
## Residuals            39 24254.7  621.92
plot (lm13)

####Q2.4 Effects of salinity and pH on percent of reproductive apices#### Different salinity terms first

Effect of pH and salinity on percent reproductive apices

lm1 <- lm(perc.ra ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = perc.ra ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##    -759.134       56.270       98.353       -7.089
summary (lm1)
## 
## Call:
## lm(formula = perc.ra ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.814 -11.201  -4.418   8.705  38.225 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -759.134    677.707  -1.120   0.2697  
## salinity      56.270     30.329   1.855   0.0713 .
## ph            98.353     85.752   1.147   0.2586  
## salinity:ph   -7.089      3.840  -1.846   0.0727 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.1 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2597, Adjusted R-squared:  0.2013 
## F-statistic: 4.444 on 3 and 38 DF,  p-value: 0.009
anova (lm1)
## Analysis of Variance Table
## 
## Response: perc.ra
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity     1  325.0  325.03  1.2543 0.269771   
## ph           1 2246.9 2246.90  8.6704 0.005493 **
## salinity:ph  1  883.0  882.95  3.4072 0.072715 . 
## Residuals   38 9847.5  259.14                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on percent reproductive apices, interaction term removed

lm2 <- lm(perc.ra ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = perc.ra ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    460.3810       0.2915     -55.9912
summary (lm2)
## 
## Call:
## lm(formula = perc.ra ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.526 -13.888  -0.801  11.568  36.644 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 460.3810   155.5581   2.960  0.00522 **
## salinity      0.2915     0.3541   0.823  0.41536   
## ph          -55.9912    19.5932  -2.858  0.00681 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1933, Adjusted R-squared:  0.152 
## F-statistic: 4.674 on 2 and 39 DF,  p-value: 0.01515
anova (lm2)
## Analysis of Variance Table
## 
## Response: perc.ra
##           Df  Sum Sq Mean Sq F value   Pr(>F)   
## salinity   1   325.0  325.03  1.1813 0.283756   
## ph         1  2246.9 2246.90  8.1664 0.006813 **
## Residuals 39 10730.5  275.14                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on percent reproductive apices: daily minimum salinity

lm3 <- lm(perc.ra ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = perc.ra ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##     476.03479        0.06173      -57.27507
summary (lm3)
## 
## Call:
## lm(formula = perc.ra ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.212 -12.607  -1.941  12.969  36.433 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   476.03479  155.85548   3.054  0.00405 **
## daily.min.sal   0.06173    0.32219   0.192  0.84906   
## ph            -57.27507   19.69417  -2.908  0.00597 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.72 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1801, Adjusted R-squared:  0.1381 
## F-statistic: 4.283 on 2 and 39 DF,  p-value: 0.02081
anova (lm3)
## Analysis of Variance Table
## 
## Response: perc.ra
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## daily.min.sal  1    30.4   30.43  0.1088 0.743281   
## ph             1  2365.3 2365.29  8.4578 0.005971 **
## Residuals     39 10906.7  279.66                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on percent reproductive apices: daily maximum salinity

lm4 <- lm(perc.ra ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = perc.ra ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      425.8758         0.7742       -53.3346
summary (lm4)
## 
## Call:
## lm(formula = perc.ra ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.080 -12.467  -1.497   9.573  33.887 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   425.8758   151.8023   2.805  0.00780 **
## daily.max.sal   0.7742     0.4233   1.829  0.07509 . 
## ph            -53.3346    19.0219  -2.804  0.00783 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.06 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2441, Adjusted R-squared:  0.2054 
## F-statistic: 6.298 on 2 and 39 DF,  p-value: 0.004262
anova (lm4)
## Analysis of Variance Table
## 
## Response: perc.ra
##               Df  Sum Sq Mean Sq F value  Pr(>F)   
## daily.max.sal  1  1220.8 1220.83  4.7353 0.03567 * 
## ph             1  2026.8 2026.82  7.8615 0.00783 **
## Residuals     39 10054.7  257.81                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on percent reproductive apices: daily salinity range

lm5 <- lm(perc.ra ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##         442.653            1.077          -53.558
summary (lm5)
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.101 -13.996  -2.112  11.653  29.149 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     442.6525   149.2169   2.967  0.00512 **
## daily.sal.range   1.0767     0.5465   1.970  0.05597 . 
## ph              -53.5578    18.8734  -2.838  0.00717 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.96 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2536, Adjusted R-squared:  0.2153 
## F-statistic: 6.625 on 2 and 39 DF,  p-value: 0.003334
anova (lm5)
## Analysis of Variance Table
## 
## Response: perc.ra
##                 Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.sal.range  1 1323.4 1323.37  5.1981 0.028161 * 
## ph               1 2050.1 2050.13  8.0528 0.007174 **
## Residuals       39 9928.9  254.59                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on percent reproductive apices: daily median salinity

lm6 <- lm(perc.ra ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = perc.ra ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      458.7750         0.3165       -55.8557
summary (lm6)
## 
## Call:
## lm(formula = perc.ra ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.306 -13.515  -0.889  11.423  36.517 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   458.7750   155.3199   2.954  0.00530 **
## daily.med.sal   0.3165     0.3535   0.895  0.37605   
## ph            -55.8557    19.5636  -2.855  0.00686 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.56 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1959, Adjusted R-squared:  0.1546 
## F-statistic: 4.749 on 2 and 39 DF,  p-value: 0.01426
anova (lm6)
## Analysis of Variance Table
## 
## Response: perc.ra
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.sal  1   369.6  369.55  1.3473 0.252799   
## ph             1  2235.8 2235.81  8.1515 0.006859 **
## Residuals     39 10697.0  274.28                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on percent reproductive apices: number of days with a daily minimun salinity less than 5

lm7 <- lm(perc.ra ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##           438.127             -0.292            -51.916
summary (lm7)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.341 -13.102  -2.535  12.241  35.112 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       438.1266   160.6456   2.727  0.00952 **
## min.daily.sal.lt5  -0.2920     0.3339  -0.874  0.38722   
## ph                -51.9161    20.4941  -2.533  0.01543 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.57 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1951, Adjusted R-squared:  0.1538 
## F-statistic: 4.727 on 2 and 39 DF,  p-value: 0.01452
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.ra
##                   Df  Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt5  1   833.6  833.61  3.0364 0.08930 .
## ph                 1  1761.8 1761.77  6.4172 0.01543 *
## Residuals         39 10707.0  274.54                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect of salinity and pH on percent reproductive apices: number of days with a daily minimun salinity less than 10

lm8 <- lm(perc.ra ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##           442.9136             -0.2187            -52.5542
summary (lm8)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.612 -13.814  -1.956  12.858  35.011 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        442.9136   162.9537   2.718  0.00975 **
## min.daily.sal.lt10  -0.2187     0.3188  -0.686  0.49674   
## ph                 -52.5542    20.8265  -2.523  0.01581 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.63 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1891, Adjusted R-squared:  0.1475 
## F-statistic: 4.548 on 2 and 39 DF,  p-value: 0.01678
anova (lm8)
## Analysis of Variance Table
## 
## Response: perc.ra
##                    Df  Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt10  1   754.4  754.42  2.7276 0.10665  
## ph                  1  1761.2 1761.20  6.3677 0.01581 *
## Residuals          39 10786.8  276.58                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm8)

Effect of salinity and pH on percent reproductive apices: number of days with a daily minimun salinity less than 15

lm9 <- lm(perc.ra ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##            458.707              -0.265             -54.426
summary (lm9)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -24.35 -14.10  -2.00  12.42  34.46 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        458.7067   155.2434   2.955  0.00528 **
## min.daily.sal.lt15  -0.2650     0.2919  -0.908  0.36947   
## ph                 -54.4264    19.7599  -2.754  0.00889 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.56 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1963, Adjusted R-squared:  0.1551 
## F-statistic: 4.763 on 2 and 39 DF,  p-value: 0.0141
anova (lm9)
## Analysis of Variance Table
## 
## Response: perc.ra
##                    Df  Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.sal.lt15  1   531.7  531.73  1.9397 0.171588   
## ph                  1  2079.7 2079.71  7.5867 0.008887 **
## Residuals          39 10690.9  274.13                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on percent reproductive apices: number of days with a daily maximum salinity less than 5

lm10 <- lm(perc.ra ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##        477.955387          -0.004578         -57.346528
summary (lm10)
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.935 -12.089  -2.399  13.371  35.997 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       477.955387 165.330932   2.891  0.00625 **
## max.daily.sal.lt5  -0.004578   0.325436  -0.014  0.98885   
## ph                -57.346528  21.110917  -2.716  0.00979 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.73 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1793, Adjusted R-squared:  0.1372 
## F-statistic: 4.261 on 2 and 39 DF,  p-value: 0.0212
anova (lm10)
## Analysis of Variance Table
## 
## Response: perc.ra
##                   Df  Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.sal.lt5  1   320.0  319.96   1.143 0.291583   
## ph                 1  2065.5 2065.54   7.379 0.009787 **
## Residuals         39 10916.9  279.92                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect of salinity and pH on percent reproductive apices: number of days with a daily maximum salinity less than 10

lm11 <- lm(perc.ra ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           454.3500             -0.1385            -54.1652
summary (lm11)
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.692 -12.804  -2.093  12.636  35.612 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        454.3500   165.5109   2.745   0.0091 **
## max.daily.sal.lt10  -0.1385     0.3304  -0.419   0.6774   
## ph                 -54.1652    21.1466  -2.561   0.0144 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.69 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.183,  Adjusted R-squared:  0.1411 
## F-statistic: 4.368 on 2 and 39 DF,  p-value: 0.01942
anova (lm11)
## Analysis of Variance Table
## 
## Response: perc.ra
##                    Df  Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt10  1   606.1  606.11  2.1751 0.14829  
## ph                  1  1828.3 1828.29  6.5609 0.01441 *
## Residuals          39 10868.0  278.67                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm11)

Effect of salinity and pH on percent reproductive apices: number of days with a daily maximum salinity less than 15

lm12 <- lm(perc.ra ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##           429.3167             -0.2456            -50.8008
summary (lm12)
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.721 -13.909  -1.778  13.073  34.964 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        429.3167   166.8886   2.572   0.0140 *
## max.daily.sal.lt15  -0.2456     0.3183  -0.772   0.4449  
## ph                 -50.8008    21.3500  -2.379   0.0223 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.6 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1917, Adjusted R-squared:  0.1502 
## F-statistic: 4.624 on 2 and 39 DF,  p-value: 0.01577
anova (lm12)
## Analysis of Variance Table
## 
## Response: perc.ra
##                    Df  Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt15  1   988.7  988.72  3.5861 0.06570 .
## ph                  1  1561.0 1560.98  5.6617 0.02233 *
## Residuals          39 10752.7  275.71                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm12)

Effect of salinity and pH on percent reproductive apices: number of days with a daily salinity range greater than 10

lm13 <- lm(perc.ra ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##           478.292663             -0.002928            -57.391466
summary (lm13)
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -26.92 -12.09  -2.40  13.36  36.00 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)   
## (Intercept)          478.292663 163.633935   2.923  0.00575 **
## daily.sal.range.gt10  -0.002928   0.327580  -0.009  0.99291   
## ph                   -57.391466  20.886545  -2.748  0.00904 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.73 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1793, Adjusted R-squared:  0.1372 
## F-statistic: 4.261 on 2 and 39 DF,  p-value: 0.0212
anova (lm13)
## Analysis of Variance Table
## 
## Response: perc.ra
##                      Df  Sum Sq Mean Sq F value   Pr(>F)   
## daily.sal.range.gt10  1   272.0  272.00  0.9717 0.330335   
## ph                    1  2113.5 2113.47  7.5503 0.009038 **
## Residuals            39 10916.9  279.92                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

Effect of salinity and pH on percent reproductive apices: number of days with a daily salinity range greater than 5

lm14 <- lm(perc.ra ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            485.8426              -0.3408             -57.6973
summary (lm14)
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.651 -13.965  -1.868  12.049  33.914 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         485.8426   152.7678   3.180  0.00288 **
## daily.sal.range.gt5  -0.3408     0.2925  -1.165  0.25102   
## ph                  -57.6973    19.3485  -2.982  0.00492 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.45 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2069, Adjusted R-squared:  0.1663 
## F-statistic: 5.088 on 2 and 39 DF,  p-value: 0.01088
anova (lm14)
## Analysis of Variance Table
## 
## Response: perc.ra
##                     Df  Sum Sq Mean Sq F value   Pr(>F)   
## daily.sal.range.gt5  1   347.3   347.3  1.2839 0.264096   
## ph                   1  2405.4  2405.4  8.8923 0.004916 **
## Residuals           39 10549.7   270.5                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent reproductive apices: daily minimum ph

lm3 <- lm(perc.ra ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = perc.ra ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     423.5584      -51.7626        0.2511
summary (lm3)
## 
## Call:
## lm(formula = perc.ra ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.457 -15.025  -2.368  14.170  38.629 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  423.5584   222.2713   1.906   0.0641 .
## daily.min.ph -51.7626    28.2430  -1.833   0.0745 .
## salinity       0.2511     0.3790   0.662   0.5116  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.5 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1018, Adjusted R-squared:  0.05573 
## F-statistic:  2.21 on 2 and 39 DF,  p-value: 0.1233
anova (lm3)
## Analysis of Variance Table
## 
## Response: perc.ra
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.ph  1  1219.7 1219.70  3.9812 0.05303 .
## salinity      1   134.4  134.42  0.4388 0.51162  
## Residuals    39 11948.3  306.37                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on percent reproductive apices: daily maximum ph

lm4 <- lm(perc.ra ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = perc.ra ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     297.9819      -35.0236        0.3006
summary (lm4)
## 
## Call:
## lm(formula = perc.ra ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -25.68 -15.49  -0.63  10.36  33.84 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  297.9819   118.6628   2.511   0.0163 *
## daily.max.ph -35.0236    14.7249  -2.379   0.0224 *
## salinity       0.3006     0.3641   0.826   0.4140  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.05 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.148,  Adjusted R-squared:  0.1043 
## F-statistic: 3.388 on 2 and 39 DF,  p-value: 0.04399
anova (lm4)
## Analysis of Variance Table
## 
## Response: perc.ra
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.max.ph  1  1771.0 1770.95  6.0942 0.01805 *
## salinity      1   198.1  198.11  0.6817 0.41401  
## Residuals    39 11333.3  290.60                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on percent reproductive apices: daily ph range

lm5 <- lm(perc.ra ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = perc.ra ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        15.9402          4.6768          0.3388
summary (lm5)
## 
## Call:
## lm(formula = perc.ra ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.918 -12.588  -5.098  11.829  37.719 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     15.9402     9.0888   1.754   0.0873 .
## daily.ph.range   4.6768     2.3969   1.951   0.0582 .
## salinity         0.3388     0.3709   0.914   0.3666  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.41 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1112, Adjusted R-squared:  0.06562 
## F-statistic:  2.44 on 2 and 39 DF,  p-value: 0.1004
anova (lm5)
## Analysis of Variance Table
## 
## Response: perc.ra
##                Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.ph.range  1  1226.2 1226.21  4.0448 0.05126 .
## salinity        1   253.0  253.01  0.8346 0.36656  
## Residuals      39 11823.2  303.16                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on percent reproductive apices: daily median ph

lm6 <- lm(perc.ra ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = perc.ra ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     398.1294      -48.0260        0.2833
summary (lm6)
## 
## Call:
## lm(formula = perc.ra ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.597 -14.516   0.319  12.003  35.804 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  398.1294   154.5400   2.576   0.0139 *
## daily.med.ph -48.0260    19.4178  -2.473   0.0178 *
## salinity       0.2833     0.3628   0.781   0.4396  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.96 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1567, Adjusted R-squared:  0.1135 
## F-statistic: 3.624 on 2 and 39 DF,  p-value: 0.03602
anova (lm6)
## Analysis of Variance Table
## 
## Response: perc.ra
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.ph  1  1909.2 1909.16  6.6374 0.01389 *
## salinity      1   175.4  175.41  0.6098 0.43957  
## Residuals    39 11217.8  287.64                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on percent reproductive apices: number of days with a daily minimun ph less than 7

lm7 <- lm(perc.ra ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = perc.ra ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          17.5787           -0.2764            0.4509
summary (lm7)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -23.07 -13.80  -5.86  11.26  36.41 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       17.5787     9.5463   1.841   0.0732 .
## min.daily.ph.lt7  -0.2764     0.3630  -0.761   0.4510  
## salinity           0.4509     0.3951   1.141   0.2607  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18.11 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03872,    Adjusted R-squared:  -0.01058 
## F-statistic: 0.7855 on 2 and 39 DF,  p-value: 0.463
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.ra
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1    87.9   87.94  0.2682 0.6075
## salinity          1   427.1  427.15  1.3028 0.2607
## Residuals        39 12787.3  327.88
plot (lm7)

Effect salinity and pH on percent reproductive apices: number of days with a daily minimun ph less than 8

lm7 <- lm(perc.ra ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = perc.ra ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##          24.0615           -0.3143            0.3995
summary (lm7)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.274 -13.202  -4.673  11.167  39.771 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       24.0615    10.0746   2.388   0.0219 *
## min.daily.ph.lt8  -0.3143     0.1760  -1.786   0.0819 .
## salinity           0.3995     0.3730   1.071   0.2907  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.54 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09818,    Adjusted R-squared:  0.05193 
## F-statistic: 2.123 on 2 and 39 DF,  p-value: 0.1333
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.ra
##                  Df  Sum Sq Mean Sq F value Pr(>F)  
## min.daily.ph.lt8  1   953.1  953.11  3.0986 0.0862 .
## salinity          1   352.9  352.92  1.1473 0.2907  
## Residuals        39 11996.3  307.60                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on percent reproductive apices: number of days with a daily maximum ph less than 7

lm10 <- lm(perc.ra ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = perc.ra ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          17.5787           -0.2764            0.4509
summary (lm10)
## 
## Call:
## lm(formula = perc.ra ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -23.07 -13.80  -5.86  11.26  36.41 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       17.5787     9.5463   1.841   0.0732 .
## max.daily.ph.lt7  -0.2764     0.3630  -0.761   0.4510  
## salinity           0.4509     0.3951   1.141   0.2607  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18.11 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03872,    Adjusted R-squared:  -0.01058 
## F-statistic: 0.7855 on 2 and 39 DF,  p-value: 0.463
anova (lm10)
## Analysis of Variance Table
## 
## Response: perc.ra
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1    87.9   87.94  0.2682 0.6075
## salinity          1   427.1  427.15  1.3028 0.2607
## Residuals        39 12787.3  327.88
plot (lm10)

Effect salinity and pH on percent reproductive apices: number of days with a daily ph range greater than 0.5

lm13 <- lm(perc.ra ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = perc.ra ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              18.2048               -0.3837                0.4838
summary (lm13)
## 
## Call:
## lm(formula = perc.ra ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.350 -14.251  -3.089  11.281  36.323 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           18.2048     9.4825   1.920   0.0622 .
## daily.ph.range.gt0.5  -0.3837     0.3406  -1.127   0.2668  
## salinity               0.4838     0.3919   1.234   0.2244  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.95 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05518,    Adjusted R-squared:  0.006732 
## F-statistic: 1.139 on 2 and 39 DF,  p-value: 0.3306
anova (lm13)
## Analysis of Variance Table
## 
## Response: perc.ra
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1    243  243.04  0.7542 0.3905
## salinity              1    491  491.03  1.5237 0.2244
## Residuals            39  12568  322.26
plot (lm13)

####Q2.5 Effects of salinity and pH on number of oogonia#### Different salinity terms first

Effect of pH and salinity on number of oogonia

lm1 <- lm(avg.oog ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = avg.oog ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##    -327.512       27.353       42.719       -3.406
summary (lm1)
## 
## Call:
## lm(formula = avg.oog ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.373 -13.535  -0.961  12.186  35.933 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -327.512    685.432  -0.478    0.636
## salinity      27.353     30.674   0.892    0.378
## ph            42.719     86.729   0.493    0.625
## salinity:ph   -3.406      3.884  -0.877    0.386
## 
## Residual standard error: 16.28 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1288, Adjusted R-squared:  0.06005 
## F-statistic: 1.873 on 3 and 38 DF,  p-value: 0.1506
anova (lm1)
## Analysis of Variance Table
## 
## Response: avg.oog
##             Df  Sum Sq Mean Sq F value Pr(>F)
## salinity     1   577.8  577.84  2.1798 0.1481
## ph           1   708.0  708.03  2.6709 0.1105
## salinity:ph  1   203.8  203.78  0.7687 0.3861
## Residuals   38 10073.3  265.09
plot (lm1)

Effect of pH and salinity on number of oogonia, interaction term removed

lm2 <- lm(avg.oog ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = avg.oog ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    258.3609       0.4595     -31.4307
summary (lm2)
## 
## Call:
## lm(formula = avg.oog ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.597 -13.156  -2.196  12.813  37.142 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 258.3609   152.2363   1.697   0.0976 .
## salinity      0.4595     0.3465   1.326   0.1925  
## ph          -31.4307    19.1748  -1.639   0.1092  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.23 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1112, Adjusted R-squared:  0.06563 
## F-statistic:  2.44 on 2 and 39 DF,  p-value: 0.1004
anova (lm2)
## Analysis of Variance Table
## 
## Response: avg.oog
##           Df  Sum Sq Mean Sq F value Pr(>F)
## salinity   1   577.8  577.84  2.1928 0.1467
## ph         1   708.0  708.03  2.6869 0.1092
## Residuals 39 10277.1  263.51
plot (lm2)

Effect and salinity and pH on number of oogonia: daily minimum salinity

lm3 <- lm(avg.oog ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = avg.oog ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      269.8840         0.3959       -32.5891
summary (lm3)
## 
## Call:
## lm(formula = avg.oog ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.028 -13.473  -1.431  11.963  38.108 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   269.8840   151.5929   1.780   0.0828 .
## daily.min.sal   0.3959     0.3134   1.263   0.2140  
## ph            -32.5891    19.1555  -1.701   0.0968 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.27 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1076, Adjusted R-squared:  0.06188 
## F-statistic: 2.352 on 2 and 39 DF,  p-value: 0.1085
anova (lm3)
## Analysis of Variance Table
## 
## Response: avg.oog
##               Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.sal  1   478.9  478.93  1.8102 0.18625  
## ph             1   765.8  765.77  2.8944 0.09685 .
## Residuals     39 10318.2  264.57                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on number of oogonia: daily maximum salinity

lm4 <- lm(avg.oog ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = avg.oog ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      247.9452         0.5765       -30.6688
summary (lm4)
## 
## Call:
## lm(formula = avg.oog ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.090 -13.720  -2.557  13.373  37.169 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   247.9452   153.3612   1.617    0.114
## daily.max.sal   0.5765     0.4277   1.348    0.185
## ph            -30.6688    19.2173  -1.596    0.119
## 
## Residual standard error: 16.22 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1125, Adjusted R-squared:  0.06697 
## F-statistic: 2.471 on 2 and 39 DF,  p-value: 0.0976
anova (lm4)
## Analysis of Variance Table
## 
## Response: avg.oog
##               Df  Sum Sq Mean Sq F value Pr(>F)
## daily.max.sal  1   630.4  630.45  2.3959 0.1297
## ph             1   670.2  670.18  2.5469 0.1186
## Residuals     39 10262.3  263.14
plot (lm4)

Effect and salinity and pH on number of oogonia: daily salinity range

lm5 <- lm(avg.oog ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##         297.548           -0.305          -34.840
summary (lm5)
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.464 -13.302  -2.901   8.653  40.074 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     297.5482   154.6217   1.924   0.0616 .
## daily.sal.range  -0.3050     0.5663  -0.538   0.5933  
## ph              -34.8400    19.5570  -1.781   0.0826 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.53 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07799,    Adjusted R-squared:  0.0307 
## F-statistic: 1.649 on 2 and 39 DF,  p-value: 0.2053
anova (lm5)
## Analysis of Variance Table
## 
## Response: avg.oog
##                 Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1    34.2   34.21  0.1251 0.72543  
## ph               1   867.5  867.54  3.1736 0.08263 .
## Residuals       39 10661.2  273.36                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on number of oogonia: daily median salinity

lm6 <- lm(avg.oog ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = avg.oog ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      257.3916         0.4742       -31.3421
summary (lm6)
## 
## Call:
## lm(formula = avg.oog ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.585 -13.355  -1.831  12.819  37.416 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   257.3916   152.0167   1.693   0.0984 .
## daily.med.sal   0.4742     0.3460   1.371   0.1783  
## ph            -31.3421    19.1476  -1.637   0.1097  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.21 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1138, Adjusted R-squared:  0.06837 
## F-statistic: 2.505 on 2 and 39 DF,  p-value: 0.09478
anova (lm6)
## Analysis of Variance Table
## 
## Response: avg.oog
##               Df  Sum Sq Mean Sq F value Pr(>F)
## daily.med.sal  1   612.1  612.10  2.3297 0.1350
## ph             1   704.0  703.97  2.6793 0.1097
## Residuals     39 10246.9  262.74
plot (lm6)

Effect of salinity and pH on number of oogonia: number of days with a daily minimun salinity less than 5

lm7 <- lm(avg.oog ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          342.6633             0.3977           -41.2798
summary (lm7)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.560 -11.497  -4.915  10.810  36.987 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       342.6633   157.9518   2.169   0.0362 *
## min.daily.sal.lt5   0.3977     0.3283   1.211   0.2330  
## ph                -41.2798    20.1505  -2.049   0.0473 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.29 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1048, Adjusted R-squared:  0.05891 
## F-statistic: 2.283 on 2 and 39 DF,  p-value: 0.1154
anova (lm7)
## Analysis of Variance Table
## 
## Response: avg.oog
##                   Df  Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt5  1    98.2   98.18  0.3699 0.54658  
## ph                 1  1113.8 1113.83  4.1967 0.04728 *
## Residuals         39 10350.9  265.41                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect of salinity and pH on number of oogonia: number of days with a daily minimun salinity less than 10

lm8 <- lm(avg.oog ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##          300.77280             0.08209           -35.57520
summary (lm8)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.080 -12.033  -3.883   9.425  39.519 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        300.77280  162.46466   1.851   0.0717 .
## min.daily.sal.lt10   0.08209    0.31789   0.258   0.7976  
## ph                 -35.57520   20.76400  -1.713   0.0946 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07272,    Adjusted R-squared:  0.02516 
## F-statistic: 1.529 on 2 and 39 DF,  p-value: 0.2294
anova (lm8)
## Analysis of Variance Table
## 
## Response: avg.oog
##                    Df  Sum Sq Mean Sq F value Pr(>F)  
## min.daily.sal.lt10  1    33.8   33.79  0.1229 0.7278  
## ph                  1   807.0  807.03  2.9354 0.0946 .
## Residuals          39 10722.1  274.93                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm8)

Effect of salinity and pH on number of oogonia: number of days with a daily minimun salinity less than 15

lm9 <- lm(avg.oog ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##            274.615              -0.168             -31.817
summary (lm9)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.652 -13.182  -2.505   9.419  40.466 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        274.6151   154.9433   1.772   0.0841 .
## min.daily.sal.lt15  -0.1680     0.2913  -0.577   0.5675  
## ph                 -31.8173    19.7217  -1.613   0.1147  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.52 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07898,    Adjusted R-squared:  0.03175 
## F-statistic: 1.672 on 2 and 39 DF,  p-value: 0.201
anova (lm9)
## Analysis of Variance Table
## 
## Response: avg.oog
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1   202.6  202.55  0.7418 0.3944
## ph                  1   710.7  710.74  2.6028 0.1147
## Residuals          39 10649.6  273.07
plot (lm9)

Effect of salinity and pH on number of oogonia: number of days with a daily maximum salinity less than 5

lm10 <- lm(avg.oog ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          358.7278             0.4094           -43.3428
summary (lm10)
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.630 -12.186  -4.173  11.258  36.898 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       358.7278   160.5726   2.234   0.0313 *
## max.daily.sal.lt5   0.4094     0.3161   1.295   0.2029  
## ph                -43.3428    20.5033  -2.114   0.0410 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.25 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1094, Adjusted R-squared:  0.06377 
## F-statistic: 2.396 on 2 and 39 DF,  p-value: 0.1043
anova (lm10)
## Analysis of Variance Table
## 
## Response: avg.oog
##                   Df  Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt5  1    85.5   85.49  0.3238 0.57261  
## ph                 1  1179.9 1179.92  4.4687 0.04097 *
## Residuals         39 10297.5  264.04                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect of salinity and pH on number of oogonia: number of days with a daily maximum salinity less than 10

lm11 <- lm(avg.oog ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           357.2367              0.3967            -43.1582
summary (lm11)
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.269 -12.129  -4.392  11.371  37.157 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        357.2367   161.4298   2.213   0.0328 *
## max.daily.sal.lt10   0.3967     0.3222   1.231   0.2256  
## ph                 -43.1582    20.6251  -2.093   0.0430 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.28 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1059, Adjusted R-squared:  0.06003 
## F-statistic: 2.309 on 2 and 39 DF,  p-value: 0.1128
anova (lm11)
## Analysis of Variance Table
## 
## Response: avg.oog
##                    Df  Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt10  1    63.6   63.58  0.2398 0.62707  
## ph                  1  1160.7 1160.73  4.3786 0.04295 *
## Residuals          39 10338.6  265.09                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm11)

Effect of salinity and pH on number of oogonia: number of days with a daily maximum salinity less than 15

lm12 <- lm(avg.oog ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##             327.57                0.20              -39.15
summary (lm12)
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.627 -11.785  -4.205  10.219  38.766 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        327.5733   165.9460   1.974   0.0555 .
## max.daily.sal.lt15   0.2000     0.3165   0.632   0.5311  
## ph                 -39.1532    21.2294  -1.844   0.0727 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.51 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08055,    Adjusted R-squared:  0.0334 
## F-statistic: 1.708 on 2 and 39 DF,  p-value: 0.1945
anova (lm12)
## Analysis of Variance Table
## 
## Response: avg.oog
##                    Df  Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt15  1     4.1    4.13  0.0152 0.90267  
## ph                  1   927.2  927.24  3.4014 0.07275 .
## Residuals          39 10631.6  272.60                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm12)

Effect of salinity and pH on number of oogonia: number of days with a daily salinity range greater than 10

lm13 <- lm(avg.oog ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##              350.036                 0.398               -42.233
summary (lm13)
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.769 -12.312  -4.169  10.793  37.080 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          350.0358   159.1529   2.199   0.0338 *
## daily.sal.range.gt10   0.3980     0.3186   1.249   0.2190  
## ph                   -42.2328    20.3146  -2.079   0.0442 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.27 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1069, Adjusted R-squared:  0.06107 
## F-statistic: 2.333 on 2 and 39 DF,  p-value: 0.1104
anova (lm13)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt10  1    91.3   91.29  0.3447 0.56049  
## ph                    1  1144.5 1144.46  4.3220 0.04425 *
## Residuals            39 10327.2  264.80                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

Effect of salinity and pH on number of oogonia: number of days with a daily salinity range greater than 5

lm14 <- lm(avg.oog ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            292.0056              -0.2252             -33.8972
summary (lm14)
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.351 -13.515  -4.114   8.747  40.354 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         292.0056   152.9886   1.909   0.0637 .
## daily.sal.range.gt5  -0.2252     0.2929  -0.769   0.4467  
## ph                  -33.8972    19.3765  -1.749   0.0881 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.47 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08499,    Adjusted R-squared:  0.03807 
## F-statistic: 1.811 on 2 and 39 DF,  p-value: 0.1769
anova (lm14)
## Analysis of Variance Table
## 
## Response: avg.oog
##                     Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt5  1   152.5  152.51  0.5622 0.45788  
## ph                   1   830.2  830.24  3.0604 0.08809 .
## Residuals           39 10580.2  271.29                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on number of oogonia: daily minimum ph

lm3 <- lm(avg.oog ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = avg.oog ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     -16.1368        3.2230        0.5193
summary (lm3)
## 
## Call:
## lm(formula = avg.oog ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.101 -14.153  -2.433  10.085  38.288 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -16.1368   213.0856  -0.076    0.940
## daily.min.ph   3.2230    27.0758   0.119    0.906
## salinity       0.5193     0.3634   1.429    0.161
## 
## Residual standard error: 16.78 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05032,    Adjusted R-squared:  0.001617 
## F-statistic: 1.033 on 2 and 39 DF,  p-value: 0.3654
anova (lm3)
## Analysis of Variance Table
## 
## Response: avg.oog
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.min.ph  1     6.8    6.83  0.0243  0.877
## salinity      1   575.0  575.00  2.0421  0.161
## Residuals    39 10981.1  281.57
plot (lm3)

Effect salinity and pH on number of oogonia: daily maximum ph

lm4 <- lm(avg.oog ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = avg.oog ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     177.6377      -20.9596        0.4616
summary (lm4)
## 
## Call:
## lm(formula = avg.oog ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -21.75 -12.67  -2.78  12.67  36.34 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  177.6377   113.6516   1.563    0.126
## daily.max.ph -20.9596    14.1030  -1.486    0.145
## salinity       0.4616     0.3487   1.324    0.193
## 
## Residual standard error: 16.33 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1009, Adjusted R-squared:  0.05479 
## F-statistic: 2.188 on 2 and 39 DF,  p-value: 0.1257
anova (lm4)
## Analysis of Variance Table
## 
## Response: avg.oog
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.max.ph  1   699.5  699.55  2.6242 0.1133
## salinity      1   467.1  467.07  1.7522 0.1933
## Residuals    39 10396.3  266.57
plot (lm4)

Effect of salinity and pH on number of oogonia: daily ph range

lm5 <- lm(avg.oog ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = avg.oog ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##         9.4404         -1.8467          0.5286
summary (lm5)
## 
## Call:
## lm(formula = avg.oog ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.295 -14.322  -1.301   9.936  37.699 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)      9.4404     8.6887   1.087    0.284
## daily.ph.range  -1.8467     2.2914  -0.806    0.425
## salinity         0.5286     0.3545   1.491    0.144
## 
## Residual standard error: 16.64 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06554,    Adjusted R-squared:  0.01762 
## F-statistic: 1.368 on 2 and 39 DF,  p-value: 0.2667
anova (lm5)
## Analysis of Variance Table
## 
## Response: avg.oog
##                Df  Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range  1   141.9  141.90  0.5122 0.4785
## salinity        1   615.9  615.91  2.2230 0.1440
## Residuals      39 10805.1  277.05
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on number of oogonia: daily median ph

lm6 <- lm(avg.oog ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = avg.oog ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     170.3808      -20.2846        0.4688
summary (lm6)
## 
## Call:
## lm(formula = avg.oog ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.567 -13.169  -2.488  11.974  37.207 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  170.3808   150.7278   1.130    0.265
## daily.med.ph -20.2846    18.9388  -1.071    0.291
## salinity       0.4688     0.3539   1.325    0.193
## 
## Residual standard error: 16.54 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07712,    Adjusted R-squared:  0.02979 
## F-statistic:  1.63 on 2 and 39 DF,  p-value: 0.2091
anova (lm6)
## Analysis of Variance Table
## 
## Response: avg.oog
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.med.ph  1   411.5  411.45  1.5037 0.2274
## salinity      1   480.3  480.28  1.7553 0.1929
## Residuals    39 10671.2  273.62
plot (lm6)

Effect salinity and pH on number of oogonia: number of days with a daily minimun ph less than 7

lm7 <- lm(avg.oog ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = avg.oog ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##           7.3470            0.4912            0.3908
summary (lm7)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.389 -11.682  -3.208   9.440  35.436 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)        7.3470     8.6029   0.854    0.398
## min.daily.ph.lt7   0.4912     0.3272   1.502    0.141
## salinity           0.3908     0.3560   1.098    0.279
## 
## Residual standard error: 16.32 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1019, Adjusted R-squared:  0.05584 
## F-statistic: 2.212 on 2 and 39 DF,  p-value: 0.123
anova (lm7)
## Analysis of Variance Table
## 
## Response: avg.oog
##                  Df  Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.ph.lt7  1   857.4  857.36  3.2198 0.08051 .
## salinity          1   320.8  320.81  1.2048 0.27909  
## Residuals        39 10384.8  266.28                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on number of oogonia: number of days with a daily minimun ph less than 8

lm7 <- lm(avg.oog ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = avg.oog ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##          7.12357           0.08695           0.50654
summary (lm7)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.945 -14.133  -1.819  10.889  39.084 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       7.12357    9.60763   0.741    0.463
## min.daily.ph.lt8  0.08695    0.16783   0.518    0.607
## salinity          0.50654    0.35567   1.424    0.162
## 
## Residual standard error: 16.73 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05647,    Adjusted R-squared:  0.008081 
## F-statistic: 1.167 on 2 and 39 DF,  p-value: 0.3219
anova (lm7)
## Analysis of Variance Table
## 
## Response: avg.oog
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt8  1    85.5   85.52  0.3057 0.5835
## salinity          1   567.4  567.40  2.0283 0.1623
## Residuals        39 10910.0  279.74
plot (lm7)

Effect salinity and pH on number of oogonia: number of days with a daily maximum ph less than 7

lm10 <- lm(avg.oog ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = avg.oog ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##           7.3470            0.4912            0.3908
summary (lm10)
## 
## Call:
## lm(formula = avg.oog ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.389 -11.682  -3.208   9.440  35.436 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)        7.3470     8.6029   0.854    0.398
## max.daily.ph.lt7   0.4912     0.3272   1.502    0.141
## salinity           0.3908     0.3560   1.098    0.279
## 
## Residual standard error: 16.32 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1019, Adjusted R-squared:  0.05584 
## F-statistic: 2.212 on 2 and 39 DF,  p-value: 0.123
anova (lm10)
## Analysis of Variance Table
## 
## Response: avg.oog
##                  Df  Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.ph.lt7  1   857.4  857.36  3.2198 0.08051 .
## salinity          1   320.8  320.81  1.2048 0.27909  
## Residuals        39 10384.8  266.28                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect salinity and pH on number of oogonia: number of days with a daily ph range greater than 0.5

lm13 <- lm(avg.oog ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = avg.oog ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##               7.9904                0.2790                0.4379
summary (lm13)
## 
## Call:
## lm(formula = avg.oog ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.899 -13.201  -1.963  10.168  36.996 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            7.9904     8.7775   0.910    0.368
## daily.ph.range.gt0.5   0.2790     0.3152   0.885    0.382
## salinity               0.4379     0.3628   1.207    0.235
## 
## Residual standard error: 16.62 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06868,    Adjusted R-squared:  0.02092 
## F-statistic: 1.438 on 2 and 39 DF,  p-value: 0.2497
anova (lm13)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1   391.8  391.76  1.4188 0.2408
## salinity              1   402.4  402.38  1.4572 0.2346
## Residuals            39 10768.8  276.12
plot (lm13)

####Q2.6 Effects of salinity and pH on percent reproductive dry weight####

Different salinity terms first

Effect of pH and salinity on percent reproductive dry weight

lm1 <- lm(perc.rdw ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = perc.rdw ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##    -603.288       43.925       77.721       -5.527
summary (lm1)
## 
## Call:
## lm(formula = perc.rdw ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -15.786  -8.517  -3.907   5.933  33.799 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -603.288    529.548  -1.139   0.2617  
## salinity      43.925     23.698   1.854   0.0716 .
## ph            77.721     67.005   1.160   0.2533  
## salinity:ph   -5.527      3.001  -1.842   0.0733 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.58 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2594, Adjusted R-squared:  0.201 
## F-statistic: 4.437 on 3 and 38 DF,  p-value: 0.009063
anova (lm1)
## Analysis of Variance Table
## 
## Response: perc.rdw
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity     1  267.3  267.28  1.6893 0.201524   
## ph           1 1302.1 1302.15  8.2299 0.006692 **
## salinity:ph  1  536.8  536.80  3.3927 0.073301 . 
## Residuals   38 6012.5  158.22                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on percent reproductive dry weight, interaction term removed

lm2 <- lm(perc.rdw ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = perc.rdw ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    347.5892       0.2777     -42.6244
summary (lm2)
## 
## Call:
## lm(formula = perc.rdw ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.873 -10.366  -2.228   8.964  29.716 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 347.5892   121.5290   2.860  0.00677 **
## salinity      0.2777     0.2766   1.004  0.32162   
## ph          -42.6244    15.3071  -2.785  0.00823 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.96 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1933, Adjusted R-squared:  0.1519 
## F-statistic: 4.673 on 2 and 39 DF,  p-value: 0.01516
anova (lm2)
## Analysis of Variance Table
## 
## Response: perc.rdw
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity   1  267.3  267.28  1.5916 0.214585   
## ph         1 1302.1 1302.15  7.7541 0.008226 **
## Residuals 39 6549.3  167.93                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on percent reproductive dry weight: daily minimum salinity

lm3 <- lm(perc.rdw ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = perc.rdw ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##     360.89713        0.09523      -43.74190
summary (lm3)
## 
## Call:
## lm(formula = perc.rdw ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.020 -10.199  -2.009   9.451  29.766 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   360.89713  122.10152   2.956  0.00527 **
## daily.min.sal   0.09523    0.25241   0.377  0.70801   
## ph            -43.74190   15.42896  -2.835  0.00722 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.1 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1755, Adjusted R-squared:  0.1332 
## F-statistic:  4.15 on 2 and 39 DF,  p-value: 0.02323
anova (lm3)
## Analysis of Variance Table
## 
## Response: perc.rdw
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.min.sal  1   45.0   45.04  0.2624 0.611352   
## ph             1 1379.6 1379.58  8.0375 0.007224 **
## Residuals     39 6694.1  171.64                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on percent reproductive dry weight: daily maximum salinity

lm4 <- lm(perc.rdw ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = perc.rdw ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      319.8808         0.6619       -40.4958
summary (lm4)
## 
## Call:
## lm(formula = perc.rdw ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.087 -10.362  -2.455   7.586  27.297 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   319.8808   118.1238   2.708  0.01000 **
## daily.max.sal   0.6619     0.3294   2.009  0.05145 . 
## ph            -40.4958    14.8018  -2.736  0.00932 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.49 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2501, Adjusted R-squared:  0.2116 
## F-statistic: 6.504 on 2 and 39 DF,  p-value: 0.003652
anova (lm4)
## Analysis of Variance Table
## 
## Response: perc.rdw
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.sal  1  862.0  862.03   5.522 0.023934 * 
## ph             1 1168.5 1168.47   7.485 0.009316 **
## Residuals     39 6088.2  156.11                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on percent reproductive dry weight: daily salinity range

lm5 <- lm(perc.rdw ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##        338.5447           0.7918         -41.1528
summary (lm5)
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.709  -8.572  -1.581   8.762  29.400 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     338.5447   117.7634   2.875  0.00652 **
## daily.sal.range   0.7918     0.4313   1.836  0.07405 . 
## ph              -41.1528    14.8951  -2.763  0.00870 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2383, Adjusted R-squared:  0.1992 
## F-statistic:   6.1 on 2 and 39 DF,  p-value: 0.004955
anova (lm5)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                 Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.sal.range  1  724.1  724.06  4.5662 0.038937 * 
## ph               1 1210.4 1210.42  7.6333 0.008697 **
## Residuals       39 6184.2  158.57                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on percent reproductive dry weight: daily median salinity

lm6 <- lm(perc.rdw ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = perc.rdw ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      345.8247         0.3052       -42.4766
summary (lm6)
## 
## Call:
## lm(formula = perc.rdw ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.622 -10.241  -2.349   8.807  29.601 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   345.8247   121.2045   2.853  0.00689 **
## daily.med.sal   0.3052     0.2758   1.107  0.27525   
## ph            -42.4766    15.2665  -2.782  0.00827 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.92 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1977, Adjusted R-squared:  0.1565 
## F-statistic: 4.804 on 2 and 39 DF,  p-value: 0.01365
anova (lm6)
## Analysis of Variance Table
## 
## Response: perc.rdw
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.sal  1  311.7  311.72  1.8663 0.179723   
## ph             1 1293.0 1293.00  7.7414 0.008275 **
## Residuals     39 6514.0  167.02                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily minimun salinity less than 5

lm7 <- lm(perc.rdw ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          333.0630            -0.2302           -39.6521
summary (lm7)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.226  -9.664  -1.050   9.636  28.404 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       333.0630   126.0122   2.643   0.0118 *
## min.daily.sal.lt5  -0.2302     0.2619  -0.879   0.3849  
## ph                -39.6521    16.0758  -2.467   0.0181 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1885, Adjusted R-squared:  0.1469 
## F-statistic: 4.531 on 2 and 39 DF,  p-value: 0.01701
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                   Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt5  1  502.9  502.95  2.9774 0.09235 .
## ph                 1 1027.7 1027.72  6.0840 0.01814 *
## Residuals         39 6588.0  168.92                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily minimun salinity less than 10

lm8 <- lm(perc.rdw ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##           336.0784             -0.1771            -40.0514
summary (lm8)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.857 -10.291  -1.575  10.111  28.304 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        336.0784   127.7850   2.630   0.0122 *
## min.daily.sal.lt10  -0.1771     0.2500  -0.708   0.4830  
## ph                 -40.0514    16.3317  -2.452   0.0188 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.04 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.183,  Adjusted R-squared:  0.1411 
## F-statistic: 4.367 on 2 and 39 DF,  p-value: 0.01944
anova (lm8)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt10  1  462.6  462.61  2.7199 0.10714  
## ph                  1 1022.9 1022.89  6.0141 0.01877 *
## Residuals          39 6633.2  170.08                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm8)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily minimun salinity less than 15

lm9 <- lm(perc.rdw ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##            351.248              -0.183             -41.927
summary (lm9)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.328 -10.602  -1.852   9.879  28.043 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        351.2484   122.0759   2.877  0.00647 **
## min.daily.sal.lt15  -0.1830     0.2295  -0.797  0.43008   
## ph                 -41.9272    15.5382  -2.698  0.01024 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.02 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1857, Adjusted R-squared:  0.144 
## F-statistic: 4.448 on 2 and 39 DF,  p-value: 0.01819
anova (lm9)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt15  1  273.8  273.79  1.6152 0.21129  
## ph                  1 1234.2 1234.17  7.2810 0.01024 *
## Residuals          39 6610.7  169.51                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily maximum salinity less than 5

lm10 <- lm(perc.rdw ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##         362.67823           -0.01384          -43.69314
summary (lm10)
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.172 -10.182  -1.526   9.378  29.077 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       362.67823  129.69542   2.796  0.00798 **
## max.daily.sal.lt5  -0.01384    0.25529  -0.054  0.95705   
## ph                -43.69314   16.56066  -2.638  0.01191 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.12 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1725, Adjusted R-squared:  0.1301 
## F-statistic: 4.066 on 2 and 39 DF,  p-value: 0.0249
anova (lm10)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                   Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt5  1  201.6  201.62  1.1705 0.28594  
## ph                 1 1199.1 1199.08  6.9610 0.01191 *
## Residuals         39 6718.0  172.26                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily maximum salinity less than 10

lm11 <- lm(perc.rdw ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           344.5771             -0.1164            -41.2532
summary (lm11)
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.755 -10.317  -1.337   9.488  28.778 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        344.5771   129.7978   2.655   0.0114 *
## max.daily.sal.lt10  -0.1164     0.2591  -0.449   0.6557  
## ph                 -41.2532    16.5837  -2.488   0.0172 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.09 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1767, Adjusted R-squared:  0.1345 
## F-statistic: 4.186 on 2 and 39 DF,  p-value: 0.02255
anova (lm11)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt10  1  374.3  374.27  2.1838 0.14750  
## ph                  1 1060.5 1060.52  6.1881 0.01724 *
## Residuals          39 6683.9  171.38                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm11)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily maximum salinity less than 15

lm12 <- lm(perc.rdw ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##           322.7235             -0.2105            -38.3160
summary (lm12)
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.793  -9.986  -1.512   9.936  28.215 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        322.7235   130.7283   2.469   0.0180 *
## max.daily.sal.lt15  -0.2105     0.2493  -0.844   0.4036  
## ph                 -38.3160    16.7240  -2.291   0.0274 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.01 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1873, Adjusted R-squared:  0.1456 
## F-statistic: 4.495 on 2 and 39 DF,  p-value: 0.01751
anova (lm12)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt15  1  632.8  632.83  3.7407 0.06038 .
## ph                  1  888.0  888.01  5.2490 0.02745 *
## Residuals          39 6597.9  169.18                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm12)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily salinity range greater than 10

lm13 <- lm(perc.rdw ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##           363.681361             -0.008953            -43.826763
summary (lm13)
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.115 -10.136  -1.567   9.364  29.088 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)   
## (Intercept)          363.681361 128.366842   2.833  0.00726 **
## daily.sal.range.gt10  -0.008953   0.256979  -0.035  0.97238   
## ph                   -43.826763  16.384986  -2.675  0.01087 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.12 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1725, Adjusted R-squared:  0.1301 
## F-statistic: 4.065 on 2 and 39 DF,  p-value: 0.02492
anova (lm13)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                      Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt10  1  167.9  167.92  0.9748 0.32958  
## ph                    1 1232.5 1232.48  7.1546 0.01087 *
## Residuals            39 6718.3  172.26                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily salinity range greater than 5

lm14 <- lm(perc.rdw ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            370.9651              -0.2824             -44.2195
summary (lm14)
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.055  -9.918  -2.218   9.416  27.376 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         370.9651   119.6034   3.102  0.00357 **
## daily.sal.range.gt5  -0.2824     0.2290  -1.233  0.22493   
## ph                  -44.2195    15.1481  -2.919  0.00580 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.88 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2035, Adjusted R-squared:  0.1627 
## F-statistic: 4.983 on 2 and 39 DF,  p-value: 0.01183
anova (lm14)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                     Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.sal.range.gt5  1  239.4  239.42  1.4440 0.236746   
## ph                   1 1412.9 1412.88  8.5213 0.005803 **
## Residuals           39 6466.4  165.81                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent reproductive dry weight: daily minimum ph

lm3 <- lm(perc.rdw ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = perc.rdw ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     331.8962      -40.9744        0.2429
summary (lm3)
## 
## Call:
## lm(formula = perc.rdw ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.871 -11.057  -2.963   8.402  31.335 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  331.8962   172.6218   1.923   0.0618 .
## daily.min.ph -40.9744    21.9342  -1.868   0.0693 .
## salinity       0.2429     0.2944   0.825   0.4143  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1123, Adjusted R-squared:  0.06683 
## F-statistic: 2.468 on 2 and 39 DF,  p-value: 0.09789
anova (lm3)
## Analysis of Variance Table
## 
## Response: perc.rdw
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.ph  1  786.3  786.29  4.2552 0.04583 *
## salinity      1  125.8  125.83  0.6809 0.41428  
## Residuals    39 7206.6  184.78                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on percent reproductive dry weight: daily maximum ph

lm4 <- lm(perc.rdw ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = perc.rdw ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     252.1492      -30.1703        0.2763
summary (lm4)
## 
## Call:
## lm(formula = perc.rdw ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.387 -11.758  -0.526   8.172  27.454 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  252.1492    90.7695   2.778  0.00837 **
## daily.max.ph -30.1703    11.2636  -2.679  0.01077 * 
## salinity       0.2763     0.2785   0.992  0.32716   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.04 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1832, Adjusted R-squared:  0.1413 
## F-statistic: 4.373 on 2 and 39 DF,  p-value: 0.01934
anova (lm4)
## Analysis of Variance Table
## 
## Response: perc.rdw
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.ph  1 1319.8 1319.82  7.7620 0.008196 **
## salinity      1  167.4  167.43  0.9847 0.327165   
## Residuals    39 6631.4  170.04                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on percent reproductive dry weight: daily ph range

lm5 <- lm(perc.rdw ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = perc.rdw ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##         9.3081          3.1006          0.3181
summary (lm5)
## 
## Call:
## lm(formula = perc.rdw ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.738  -9.830  -4.899   6.934  33.163 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)      9.3081     7.1632   1.299    0.201
## daily.ph.range   3.1006     1.8891   1.641    0.109
## salinity         0.3181     0.2923   1.088    0.283
## 
## Residual standard error: 13.72 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09541,    Adjusted R-squared:  0.04902 
## F-statistic: 2.057 on 2 and 39 DF,  p-value: 0.1415
anova (lm5)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.ph.range  1  551.6  551.59  2.9292 0.09493 .
## salinity        1  223.0  223.01  1.1843 0.28317  
## Residuals      39 7344.1  188.31                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on percent reproductive dry weight: daily median ph

lm6 <- lm(perc.rdw ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = perc.rdw ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     326.6628      -39.8913        0.2645
summary (lm6)
## 
## Call:
## lm(formula = perc.rdw ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.8236 -10.9259   0.1617   8.6573  29.1252 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  326.6628   118.8742   2.748  0.00903 **
## daily.med.ph -39.8913    14.9364  -2.671  0.01098 * 
## salinity       0.2645     0.2791   0.948  0.34901   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.05 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1824, Adjusted R-squared:  0.1405 
## F-statistic: 4.352 on 2 and 39 DF,  p-value: 0.01968
anova (lm6)
## Analysis of Variance Table
## 
## Response: perc.rdw
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.ph  1 1328.3 1328.32  7.8049 0.008036 **
## salinity      1  152.9  152.92  0.8985 0.349014   
## Residuals    39 6637.5  170.19                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on percent reproductive dry weight: number of days with a daily minimun ph less than 7

lm7 <- lm(perc.rdw ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          10.5825           -0.2329            0.4046
summary (lm7)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.367 -10.370  -5.419   7.118  31.574 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       10.5825     7.4157   1.427    0.162
## min.daily.ph.lt7  -0.2329     0.2820  -0.826    0.414
## salinity           0.4046     0.3069   1.318    0.195
## 
## Residual standard error: 14.07 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04954,    Adjusted R-squared:  0.0008036 
## F-statistic: 1.016 on 2 and 39 DF,  p-value: 0.3713
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1   58.4   58.40  0.2952 0.5900
## salinity          1  343.8  343.84  1.7378 0.1951
## Residuals        39 7716.5  197.86
plot (lm7)

Effect salinity and pH on percent reproductive dry weight: number of days with a daily minimun ph less than 8

lm7 <- lm(perc.rdw ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##          16.3081           -0.2758            0.3618
summary (lm7)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.579  -9.660  -3.948   9.117  35.019 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       16.3081     7.7483   2.105   0.0418 *
## min.daily.ph.lt8  -0.2758     0.1353  -2.038   0.0484 *
## salinity           0.3618     0.2868   1.261   0.2147  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.49 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.126,  Adjusted R-squared:  0.08116 
## F-statistic: 2.811 on 2 and 39 DF,  p-value: 0.07239
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                  Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.ph.lt8  1  733.3  733.32  4.0304 0.05165 .
## salinity          1  289.5  289.46  1.5909 0.21469  
## Residuals        39 7095.9  181.95                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on percent reproductive dry weight: number of days with a daily maximum ph less than 7

lm10 <- lm(perc.rdw ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          10.5825           -0.2329            0.4046
summary (lm10)
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.367 -10.370  -5.419   7.118  31.574 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       10.5825     7.4157   1.427    0.162
## max.daily.ph.lt7  -0.2329     0.2820  -0.826    0.414
## salinity           0.4046     0.3069   1.318    0.195
## 
## Residual standard error: 14.07 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04954,    Adjusted R-squared:  0.0008036 
## F-statistic: 1.016 on 2 and 39 DF,  p-value: 0.3713
anova (lm10)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                  Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1   58.4   58.40  0.2952 0.5900
## salinity          1  343.8  343.84  1.7378 0.1951
## Residuals        39 7716.5  197.86
plot (lm10)

Effect salinity and pH on percent reproductive dry weight: number of days with a daily ph range greater than 0.5

lm13 <- lm(perc.rdw ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = perc.rdw ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              11.1434               -0.3310                0.4343
summary (lm13)
## 
## Call:
## lm(formula = perc.rdw ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.522 -10.504  -4.629   7.033  30.710 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           11.1434     7.3481   1.517    0.137
## daily.ph.range.gt0.5  -0.3310     0.2639  -1.254    0.217
## salinity               0.4343     0.3037   1.430    0.161
## 
## Residual standard error: 13.91 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07042,    Adjusted R-squared:  0.02274 
## F-statistic: 1.477 on 2 and 39 DF,  p-value: 0.2408
anova (lm13)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1  176.0  176.02  0.9096 0.3461
## salinity              1  395.7  395.66  2.0446 0.1607
## Residuals            39 7547.0  193.51
plot (lm13)

####Looking at water temperature#### Effect of water temperature and salinity on density

lm6 <- lm(no.fuc.q ~ salinity + water.temp + salinity:water.temp, data =all)  
lm6
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + water.temp + salinity:water.temp, 
##     data = all)
## 
## Coefficients:
##         (Intercept)             salinity           water.temp  
##            177.7137              -9.8981             -11.6758  
## salinity:water.temp  
##              0.7257
summary (lm6)
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + water.temp + salinity:water.temp, 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.221 -19.358  -4.583  14.621  92.281 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         177.7137    76.9094   2.311  0.02456 * 
## salinity             -9.8981     3.1816  -3.111  0.00293 **
## water.temp          -11.6758     5.3644  -2.177  0.03375 * 
## salinity:water.temp   0.7257     0.2155   3.368  0.00138 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 27.75 on 56 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3961, Adjusted R-squared:  0.3637 
## F-statistic: 12.24 on 3 and 56 DF,  p-value: 2.878e-06
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                     Df Sum Sq Mean Sq F value    Pr(>F)    
## salinity             1   5692  5691.8  7.3903  0.008713 ** 
## water.temp           1  13860 13859.8 17.9959 8.385e-05 ***
## salinity:water.temp  1   8735  8735.0 11.3418  0.001376 ** 
## Residuals           56  43129   770.2                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of water temperature, phand salinity on density

lm7 <- lm(no.fuc.q ~ salinity + water.temp +ph + salinity:water.temp:ph + salinity:ph + salinity:water.temp+ water.temp:ph, data =all)  
lm7
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + water.temp + ph + salinity:water.temp:ph + 
##     salinity:ph + salinity:water.temp + water.temp:ph, data = all)
## 
## Coefficients:
##            (Intercept)                salinity              water.temp  
##                9750.40                 -611.25                 -609.19  
##                     ph             salinity:ph     salinity:water.temp  
##               -1212.27                   76.19                   36.92  
##          water.temp:ph  salinity:water.temp:ph  
##                  75.56                   -4.58
summary (lm7)
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + water.temp + ph + salinity:water.temp:ph + 
##     salinity:ph + salinity:water.temp + water.temp:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -52.866 -21.189  -3.661  13.849  89.138 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)             9750.399   8856.496   1.101    0.278
## salinity                -611.250    498.721  -1.226    0.228
## water.temp              -609.193    540.711  -1.127    0.267
## ph                     -1212.269   1122.342  -1.080    0.287
## salinity:ph               76.190     63.148   1.207    0.235
## salinity:water.temp       36.919     29.164   1.266    0.213
## water.temp:ph             75.563     68.527   1.103    0.277
## salinity:water.temp:ph    -4.580      3.692  -1.241    0.223
## 
## Residual standard error: 30.95 on 37 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.439,  Adjusted R-squared:  0.3329 
## F-statistic: 4.137 on 7 and 37 DF,  p-value: 0.001894
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                        Df Sum Sq Mean Sq F value    Pr(>F)    
## salinity                1  12797 12797.3 13.3595 0.0007933 ***
## water.temp              1   5675  5675.2  5.9245 0.0198778 *  
## ph                      1    377   377.4  0.3939 0.5340958    
## salinity:ph             1      2     1.7  0.0018 0.9662474    
## salinity:water.temp     1   7366  7366.4  7.6900 0.0086463 ** 
## water.temp:ph           1     45    44.7  0.0466 0.8302489    
## salinity:water.temp:ph  1   1475  1474.5  1.5393 0.2225322    
## Residuals              37  35443   957.9                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

####Trying splines#### Try a natural spline –> need to look into what this means

library (splines)
splinefit1 <- lm (avg.oog ~ ns(salinity, knot = median (salinity)) + ns(ph, knot = median(ph)), data = all)
summary (splinefit1)
## 
## Call:
## lm(formula = avg.oog ~ ns(salinity, knot = median(salinity)) + 
##     ns(ph, knot = median(ph)), data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.597 -13.156  -2.196  12.813  37.142 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                              24.30      10.26   2.368   0.0229 *
## ns(salinity, knot = median(salinity))    15.55      11.73   1.326   0.1925  
## ns(ph, knot = median(ph))               -30.58      18.65  -1.639   0.1092  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.23 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1112, Adjusted R-squared:  0.06563 
## F-statistic:  2.44 on 2 and 39 DF,  p-value: 0.1004
anova (splinefit1)
## Analysis of Variance Table
## 
## Response: avg.oog
##                                       Df  Sum Sq Mean Sq F value Pr(>F)
## ns(salinity, knot = median(salinity))  1   577.8  577.84  2.1928 0.1467
## ns(ph, knot = median(ph))              1   708.0  708.03  2.6869 0.1092
## Residuals                             39 10277.1  263.51
plot (splinefit1)

splinefit2 <- lm (avg.oog ~ ns(salinity, df = 2) + ns(ph, df =2), data = all)
summary (splinefit2)
## 
## Call:
## lm(formula = avg.oog ~ ns(salinity, df = 2) + ns(ph, df = 2), 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.616 -13.575  -2.146  13.773  35.987 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)             24.306     16.020   1.517    0.138
## ns(salinity, df = 2)1    8.505     20.612   0.413    0.682
## ns(salinity, df = 2)2   10.675      9.337   1.143    0.260
## ns(ph, df = 2)1        -22.333     22.781  -0.980    0.333
## ns(ph, df = 2)2        -23.535     15.472  -1.521    0.137
## 
## Residual standard error: 16.52 on 37 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1264, Adjusted R-squared:  0.03193 
## F-statistic: 1.338 on 4 and 37 DF,  p-value: 0.2742
anova (splinefit2)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## ns(salinity, df = 2)  2   695.3  347.63  1.2733 0.2919
## ns(ph, df = 2)        2   766.0  383.00  1.4028 0.2587
## Residuals            37 10101.7  273.02
plot (splinefit2)

splinefit3 <- lm (avg.oog ~ ns(salinity, df = 3) + ns(ph, df =3), data = all)
summary (splinefit3)
## 
## Call:
## lm(formula = avg.oog ~ ns(salinity, df = 3) + ns(ph, df = 3), 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.907 -12.673  -2.719  14.307  33.941 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)             20.354     17.965   1.133    0.265
## ns(salinity, df = 3)1    7.214     10.587   0.681    0.500
## ns(salinity, df = 3)2    3.208     27.581   0.116    0.908
## ns(salinity, df = 3)3    9.209      9.463   0.973    0.337
## ns(ph, df = 3)1        -15.979     12.641  -1.264    0.215
## ns(ph, df = 3)2         -4.894     30.225  -0.162    0.872
## ns(ph, df = 3)3        -17.768     17.181  -1.034    0.308
## 
## Residual standard error: 16.82 on 35 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1433, Adjusted R-squared:  -0.003618 
## F-statistic: 0.9754 on 6 and 35 DF,  p-value: 0.4563
anova (splinefit3)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df Sum Sq Mean Sq F value Pr(>F)
## ns(salinity, df = 3)  3  802.3  267.45  0.9449 0.4295
## ns(ph, df = 3)        3  854.1  284.69  1.0058 0.4018
## Residuals            35 9906.5  283.04
plot (splinefit3)

splinefit4 <- lm (avg.oog ~ ns(salinity, df = 2) + ns(ph, df =3), data = all)
summary (splinefit4)
## 
## Call:
## lm(formula = avg.oog ~ ns(salinity, df = 2) + ns(ph, df = 3), 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.480 -12.386  -2.965  14.099  34.154 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)             19.477     17.138   1.136    0.263
## ns(salinity, df = 2)1    6.320     20.875   0.303    0.764
## ns(salinity, df = 2)2    9.721      9.451   1.029    0.311
## ns(ph, df = 3)1        -16.406     12.361  -1.327    0.193
## ns(ph, df = 3)2         -4.890     29.819  -0.164    0.871
## ns(ph, df = 3)3        -18.287     16.783  -1.090    0.283
## 
## Residual standard error: 16.6 on 36 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1424, Adjusted R-squared:  0.02324 
## F-statistic: 1.195 on 5 and 36 DF,  p-value: 0.3311
anova (splinefit4)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df Sum Sq Mean Sq F value Pr(>F)
## ns(salinity, df = 2)  2  695.3  347.63  1.2620 0.2953
## ns(ph, df = 3)        3  950.8  316.93  1.1505 0.3420
## Residuals            36 9916.9  275.47
plot (splinefit4)